Surgical and non‐surgical interventions for primary and salvage treatment of growth hormone‐secreting pituitary adenomas in adults

Abstract

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows:

To assess the effects of surgical and non‐surgical interventions for primary and salvage treatment of GH‐secreting pituitary adenomas in adults.

Background

Description of the condition

Pituitary tumours account for 10% to 15% of all diagnosed intracranial tumours, 90% of which are adenomas (Villwock 2014). Pituitary adenomas are classified as microadenomas (< 10 mm), macroadenomas (≥ 10 mm), and giant adenomas (≥ 40 mm) (Buchfelder 2014). Current prevalence studies suggest that approximately two‐thirds of pituitary adenomas are symptomatic due to hypersecretion of hormones. Growth hormone (GH)‐secreting pituitary adenomas account for 20% of pituitary adenomas and are the root cause of 95% of the endocrine disorder, acromegaly (Melmed 2006; Melmed 2009a). Multinational population‐based studies estimate acromegaly to have a total prevalence of 2.8 to 13.7 cases per 100,000 people and an annual incidence rate between 0.2 and 1.1 cases per 100,000 people. The highest prevalence has been observed in Iceland at 13.3 to 13.7 cases per 100,000 (Agustsson 2015; Hoskuldsdottir 2015; Lavrentaki 2016). The median age at diagnosis is in the fifth decade of life and ranges between 40.5 and 47 years (males: 36.5 to 48.5 years; females: 38 to 56 years) (Kreutzer 2001; Lavrentaki 2016; Melmed 2009a). These tumours are comprised of cells with sparsely or densely granulated cytoplasm and may secrete GH alone or a mixture of cells secreting either GH or prolactin (Ben‐Shlomo 2008).

Growth hormone hypersecretion leads to overproduction of insulin‐like growth factor 1 (IGF‐1), leading to multisystem disease, as well as increased morbidity and mortality (Vilar 2017; Zahr 2018). Insidious clinical manifestations of acromegaly include somatic effects of acromegaly, such as coarse facial features, thickened soft tissues and lips, menstrual disturbances, erectile dysfunction, hyperhidrosis, and enlarged limbs, as well as local tumour effects, such as headaches, visual field defects, and cranial neuropathies (Kreutzer 2001; Melmed 2009a; Melmed 2016; Vilar 2017). All patients with GH hypersecretion should be evaluated for associated comorbidities, including diabetes mellitus, hypertension, osteoarthritis, and sleep apnoea, resultant from chronic GH excess (Katznelson 2014; Rosario 2011; Sesmilo 2017; Zahr 2018). Morbidity associated with neoplasms is higher in people with GH‐secreting adenomas. The odds of hyperplastic polyps, colon adenomas, and colon cancer are higher in patients with GH‐secreting tumours (odds ratio (OR): hyperplastic polyps 3.7; colon adenomas 2.5; colon cancer 4.4) (Dworakowska 2010; Molitch 2017; Rokkas 2008). Thyroid dysfunction in acromegaly is associated with increased risks of thyroid nodules (OR 3.6) and thyroid cancer (OR 7.9) (Molitch 2017; Wolinksi 2014). Acromegaly‐related cardiac disease includes cardiac valvular disease, arrhythmias, heart failure, and cardiomyopathy (Ben‐Shlomo 2008; Colao 2004; Molitch 2017). In approximately 30% to 50% of patients, co‐secretion of prolactin with GH by the tumour results in signs and symptoms of hyperprolactinaemia, including galactorrhoea, osteoporosis, and oligomenorrhoea (Kreutzer 2001; Melmed 2009a). Recognition of acromegaly‐associated features and other symptoms is critical in the prompt diagnosis of patients (Katznelson 2014; Melmed 2006). Acromegaly is associated with a two‐fold increase in mortality compared to healthy people, primarily due to cardiovascular disease (Holdaway 2008). Mortality in this patient population returns to that of the normal population after appropriate treatment and biochemical normalisation (Dekkers 2008; Holdaway 2008; Molitch 2017; Zahr 2018).

Biochemical and radiographic investigations are performed to confirm the diagnosis of GH‐secreting pituitary adenomas. Increased secretion of GH stimulates production of IGF‐1 from the liver and other tissues (Melmed 2006). An elevated age‐adjusted IGF‐1 level is a reliable indicator, with 90% specificity for a GH‐secreting pituitary adenoma (Katznelson 2014). An oral glucose tolerance test (OGTT) can be performed to further evaluate suspicious clinical findings and equivocal laboratory findings. Growth hormone levels that cannot be suppressed below less than 1 µg/L by hyperglycaemia are suggestive of a GH‐secreting tumour (Katznelson 2014; Melmed 2016; Molitch 2017).

Magnetic resonance imaging (MRI) is performed to confirm the tumour diagnosis, quantify tumour dimensions, characterize tumour invasion, and for posttreatment monitoring (Chanson 2009; Molitch 2017; Raverot 2018; Zahr 2018). MRI features of GH‐secreting adenomas correlate with tumour behavioral patterns and predictors of response to treatment (Katznelson 2014; Zahr 2018). Hypointense signal on T2‐weighted MRI is observed in over 50% of GH‐secreting adenomas and correlates with densely‐granulated pattern by immunohistochemistry as opposed to sparsely‐granulated pattern characteristic of hyperintense signal (Heck 2016; Potorac 2015; Tortora 2019). Although T2‐hypointense adenomas have a tendency to hypersecrete GH compared to T2‐isointense and T2‐hyperintense adenomas, these tumours have been found to be smaller, to less frequently invade the cavernous sinus, and to demonstrate a better biochemical response to both primary and adjunctive somatostatin analog treatment (Heck 2016; Potorac 2015; Puig‐Domingo 2010; Shen 2016; Tortora 2019). A consistent correlation between lower T2‐signal intensity and tumour shrinkage in response to somatostatin analogs has not been observed in previous studies (Potorac 2016; Shen 2016; Zahr 2018). Computerised tomography is indicated for surgical planning, evaluation of bone invasion, or when MRI is contraindicated (Katznelson 2011; Raverot 2018).

Description of the intervention

Treatment of GH‐secreting tumours is intended to reduce associated morbidity and mortality, and improve the quality of life of affected patients. The two main goals of treatment are to achieve biochemical control, defined as a reduction in GH levels to less than 1 μg/L and IGF‐1 levels to the normal age‐adjusted range, and to eliminate the established tumour (Katznelson 2014; Molitch 2017).

Surgical interventions

Surgical intervention is recommended as primary therapy for GH‐secreting tumours using minimally invasive surgical techniques, including endoscopic transsphenoidal techniques (Hussein 2019; Melmed 2016; Raverot 2018). Endoscopic transsphenoidal resection of pituitary tumours was popularised in the late 1990s by Jho and Carrau from the University of Pittsburgh Medical Center (Jho 1997). Their work was later continued by Kassam and colleagues in the early 2000s (Cavallo 2005; Kassam 2005a; Kassam 2005b; Kassam 2005c; Kassam 2005d; Kassam 2005e). Demonstrating comparable outcomes to open surgical approaches with reduced complications, minimally invasive approaches to pituitary tumours have quickly become the mainstay of surgical interventions for pituitary adenomas (Komotar 2012). There is no definitive evidence of superiority of the endoscopic versus the microscopic approach with regard to short‐ and long‐term remission rates, recurrence, or complications (Ammirati 2013; Katznelson 2014;Phan 2017).

Surgery is effective at achieving biochemical control and complete tumour excision in 80% to 90% of patients with microadenomas, 40% to 60% of patients with macroadenomas, and 50% of patients that undergo repeat surgery (Abu Dabrh 2014; Katznelson 2014; Mercado 2014; Wilson 2013). Higher remission rates are achieved in people with microadenomas, GH levels below 20 ng/mL, absent cavernous sinus invasion, extrapseudocapsular resection, and surgeons with higher surgical case volume (Anik 2017; Schöfl 2013).

Pharmacological interventions

Pharmacological therapy can be used for adjuvant or, less commonly, first‐line therapy in the management of GH‐secreting tumours (Guistina 2014; Kreutzer 2001; Melmed 2009a). Treatment options for pharmacological management of GH‐secreting tumours include: long‐acting somatostatin analogs such as octreotide, lanreotide, and pasireotide; dopamine agonists such as bromocriptine and cabergoline; and more recently GH‐receptor antagonists, such as pegvisomant (Molitch 2017). Biochemical remission and tumour reduction in response to somatostatin analogs varies widely from 20% to 75% (Katznelson 2011; Melmed 2010; Mercado 2007; Salvatori 2014; Zahr 2018). Approximately 70% of patients receiving somatostatin analogs have GH levels below 2.5 ng/mL and normalised IGF‐1, and maximal benefit may be achieved after 10 years of therapy (Melmed 2009b). In a recent clinical trial of treatment‐naive patients, lanreotide was found to achieve normalisation of IGF‐1 combined with GH levels below 2.5 ng/mL in 43.5% of patients and ≥ 20% reduction in tumour volume in 62.9% of patients (Caron 2014). Pasireotide has demonstrated superior efficacy to achieve biochemical control (31% of patients) when compared to octreotide (19%) (Colao 2014; Molitch 2017). A more favourable response to somatostatin analogs is observed in patients with smaller tumours, lower baseline GH and IGF‐1 levels, and hypointense T2‐weighted tumours on MRI images, which correlates with densely‐granulated adenomas (Katznelson 2014). Dopamine agonists are predominantly used as a treatment adjuncts in the management of GH‐secreting adenomas with mild disease (defined as IGF‐1 levels less than two times the upper limit of normal) and in cases of co‐secretion with prolactin (Katznelson 2011; Guistina 2014). Bromocriptine therapy alone has been found to suppress GH levels to less than 5 µg/L in less than 15% of patients. In 30% of patients, cabergoline is effective in suppressing GH to less than 2 µg/L and normalising IGF‐1 (Ben‐Shlomo 2008; Sabino 2010). When used in combination with somatostatin analogs, the clinical efficacy of cabergolines to achieve biochemical control increased to 52%, with remission rates dependent on baseline IGF‐1, baseline prolactin, and treatment duration (Sandret 2011). Patients treated for 12 months or more with pegvisomant achieve a normal serum IGF‐1 concentration in 97% of cases with remission rates decreasing to 63% by five years (Trainer 2000; van der Lely 2001; van der Lely 2012). Combining medical therapies may improve efficacy, reduce side effects associated with an individual medication, and decrease the frequency of injections and total drug dose. As such, pegisomant or cabergoline are recommended to supplement somatostatin analogs in patients with partial response (Franck 2016; Katznelson 2014; Sandret 2011).

Radiotherapy interventions

Primary or adjuvant radiation therapy by stereotactic radiosurgery, proton‐beam, or fractionated radiation therapy can arrest GH‐secreting tumour growth or reduce the dose of maintenance pharmacological therapy (Loeffler 2011; Melmed 2016). Radiation therapy is primarily used in patients with GH‐secreting tumours refractory to pharmacological and surgical interventions (Raverot 2018; Shih 2008). In this setting, radiation has demonstrated effective tumour control and often biochemical remission (Shih 2008). Fractionated radiation therapy will lead to a decline in GH levels to less than 10 µg/L at 10 years in 70% of patients and up to 87% at 15 years (Jenkins 2006; Loeffler 2011). Higher remission rates were observed in patients with lower baseline hormone levels (GH and IGF‐1) and smaller tumour size (Diallo 2015). Stereotactic radiosurgery leads to faster biochemical control, with normalisation of IGF‐1 and GH in 65% of patients after a median follow‐up of 61.5 months (Lee 2014). Favourable prognostic factors for efficacy of stereotactic radiosurgery include a higher margin radiation, higher maximum dose, and lower initial GH/IGF‐1 levels (Zahr 2018). As current literature shows, stereotactic radiosurgery demonstrates higher remission rates, significantly lower follow‐up IGF‐I level, and a lower incidence of hypopituitarism compared to conventional radiotherapy (Abu Dabrh 2015).

Adverse effects of the intervention

Hypopituitarism occurs in approximately 20% of patients that undergo surgical resection of pituitary tumours, while 10% of patients may develop permanent diabetes insipidus, a postoperative cerebrospinal fluid (CSF) leak, haemorrhage, or meningitis (Kreutzer 2001; Melmed 2016). Surgical intervention is associated with an estimated 2% to 8% five‐year recurrence rate which may be due to incomplete resection of pituitary adenomatous tissue or seeding of functional tumour tissue (Katznelson 2014; Melmed 2016).

Dopamine agonists can result in adverse effects including nausea, vomiting, constipation, dizziness, headache, and postural hypotension (Molitch 2017; Sherlock 2009). Somatostatin analog therapy can be complicated by abdominal cramps, flatulence, diarrhoea, gall bladder stones, and alopecia (Katznelson 2014). Reported adverse effects of GH receptor antagonists include elevated hepatic transaminases, as well as local injection site inflammation and lipodystrophy (Biering 2006; Bonert 2008).

The most common adverse effect of pituitary irradiation is hypopituitarism. Radiation induced hypopituitarism occurs in approximately 20% of patients at five years. Within 10 years after radiation, up to 80% of patients may have gonadotroph, somatotroph, thyrotroph, or corticotroph deficits (Castinetti 2010; Melmed 2016). Radiation‐induced optic neuritis is reported in less than 1.5% of patients at 10 years. Secondary tumours occur in less than 2% of patients at 20 years following radiation therapy (Loeffler 2011).

How the intervention might work

First‐line treatment for GH‐secreting adenomas is surgical resection of the tumour through minimally‐invasive treatment techniques (Katznelson 2014; Melmed 2006). Transsphendoial surgical resection involves endonasal or microscopic approach to the sella to remove the tumour, while minimising damage to the surrounding structures or endogenous pituitary function (Swearingen 2012).

Alternatively, GH‐secreting tumours may be pharmacologically controlled with long‐acting somatostatin analogs, dopamine agonists, or GH‐receptor antagonists with good effect (Molitch 2017). Long‐acting somatostatin analogs suspend GH hypersecretion by binding to somatostatin receptors 1 and 5 (Chanson 2015). Dopamine agonists bind to dopamine D2 receptors and suppress GH hypersecretions in approximately 15% of patients (Jaffe 1992; Stewart 2000). In contrast to somatostatin analogs and dopamine agonists, genetically engineered GH receptor antagonists inhibit GH action rather than secretion (Stewart 2000).

Radiation therapy delivers high‐energy ionising radiation to deep tissues by megavoltage techniques to provide maximal localised necrotising radiation to the pituitary lesion while minimally exposing surrounding normal structures to radiation damage (Castinetti 2010). Radiation therapy is primarily used in patients with acromegaly refractory to pharmacological and surgical interventions. In this setting, radiation has demonstrated effective tumour control and often biochemical remission (Shih 2008).

Why it is important to do this review

Growth hormone‐secreting pituitary adenoma is a rare but severe endocrine disease. The deleterious effects of chronic GH excess are associated with mass tumour effects, dysmorphic craniofacial features, cardiovascular, respiratory and metabolic dysfunction, arthropathies, chronic disability and impaired quality of life (Colao 2004; Kreutzer 2001; Melmed 2009a; Melmed 2016; Zahr 2018). Elevated mortality rates are directly related to GH levels, necessitating effective tumour and biochemical control (Dekkers 2008; Holdaway 2008; Melmed 2016). Treatment of GH‐secreting tumours is intended to achieve long‐term biochemical tumour control, reduction of systemic complications, reduce tumour volume, preservation of pituitary function, decrease mortality and improve quality of life. Surgery, medical therapy, and radiotherapy are the current multimodal treatment options available for the management of GH‐secreting adenomas but there is continued dispute regarding their role and effective treatment algorithms in the management of treatment‐naive and recurrent or residual disease (Guistina 2014; Katznelson 2014; Wilson 1990). Surgery offers immediate lowering of GH levels, and provides tissue for confirmatory pathologic analysis. With a remission rate of more than 85% for microadenomas and more than 40% for macroadenomas, surgery is the current recommended primary therapy for patients with GH‐secreting tumours (Katznelson 2014).

Pharmacological therapy is recognised to improve surgical outcomes in selected settings (Katznelson 2014). Pre‐operative pharmacological therapy has been found to result in tumour shrinkage and improve surgical remission rates in selected prospective studies (Carlsen 2008; Fleseriu 2015; Shen 2010; Zhang 2015). However, current guidelines do not recommend the routine use of pre‐operative pharmacological therapy in the management of GH‐secreting pituitary tumours.

Salvage therapy can be offered in the form of postoperative pharmacological therapy, reoperation or radiation therapy for persistent disease following surgical resection for disease control (Katznelson 2014). However, clear guidance around salvage therapy and supporting evidence is lacking.

A systematic review of pharmacological treatment versus surgical treatment for treatment‐naive acromegaly was performed in 2014 (Abu Dabrh 2014). The systematic review revealed that surgery offered a higher remission rate as compared to pharmacological therapy, but it only included two randomized controlled trials (RCTs) and 33 observational studies. This review did not address the question of combination therapies. Since then, new clinical trials have contributed to ongoing debate on optimising management of GH‐secreting adenomas (Fahlbusch 2017).

Due to conflicting data in the current literature regarding the best approach and sequence of non‐surgical and surgical management of GH‐secreting adenomas, a synthesis of the evidence on the most effective and safe management is needed to guide decision‐making and inform the shift towards individualised patient treatment. The purpose of this systematic review is to fill this knowledge gap and provide physicians with an evidence‐based treatment approach to GH‐secreting adenomas that integrates the current literature.

Objectives

To assess the effects of surgical and non‐surgical interventions for primary and salvage treatment of GH‐secreting pituitary adenomas in adults.

Methods

Criteria for considering studies for this review

Types of studies

We will include RCTs.

Types of participants

We will include adults (older than 18 years) with GH‐secreting pituitary adenomas.

Diagnostic criteria for GH‐secreting pituitary adenomas

The diagnosis for GH‐secreting adenomas of the pituitary gland should be established by both laboratory and imaging studies. A diagnosis of a GH‐secreting adenoma is made based on an elevated IGF‐1 level, as matched for age and gender, and a failure to suppress GH in response to an OGTT to a level of less than 1 μg/L (Carmichael 2009). Tumours of the pituitary gland are best visualised with MRI. T1‐weighted sections in the coronal and sagittal plane both before and after gadolinium pentetic acid contrast administration will distinguish most pituitary masses (Witte 2001). After gadolinium injection, pituitary microadenomas appear hypodense compared to the normal gland (Turner 2000).

Changes in diagnostic criteria may have produced significant variability in the clinical characteristics of the participants included, as well as in the results obtained (which we will investigate using sensitivity analysis).

Types of interventions

We plan to investigate the following comparisons of interventions in the management of treatment‐naive GH adenomas or salvage treatment of GH adenomas:

• surgery (endoscopic or microscopic transsphenoidal surgery) alone compared with:

  • pharmacological therapy (dopamine agonists, somatostatin analogs, or GH receptor blocker) alone;

  • radiation therapy (radiosurgery, stereotactic radiotherapy, or conventional radiation therapy) alone;

  • combination therapy:

    • preoperative pharmacological therapy (maximum six months) and surgery; or

    • surgery and postoperative pharmacological therapy; or

    • surgery and postoperative radiation therapy; or

    • pretreatment with pharmacological therapy followed by radiation therapy; or

    • radiation therapy followed by pharmacological therapy;

• pharmacological therapy alone compared with:

  • radiation therapy;

  • combination therapy:

    • preoperative pharmacological therapy (maximum six months) and surgery;

    • surgery and postoperative pharmacological therapy; or

    • surgery and postoperative radiation therapy; or

    • pretreatment with pharmacological therapy followed by radiation therapy; or

    • radiation therapy followed by pharmacological therapy;

• surgery and postoperative medical therapy compared with:

  • preoperative pharmacological therapy and surgery; or

  • surgery and postoperative radiation therapy; or

  • pretreatment with pharmacological therapy followed by radiation therapy; or

  • radiation therapy followed by pharmacological therapy;

• pre‐operative pharmacological therapy (maximum six months) and surgery compared with:

  • surgery and postoperative radiation therapy; or

  • radiation therapy followed by pharmacological therapy.

Concomitant interventions will have to be identical in both the intervention and comparator groups to establish fair comparisons. If a study includes multiple arms, we will include any arm that meets the inclusion criteria for this review.

Minimum duration of intervention

Minimum duration of intervention will be 12 weeks for pharmacological therapy and five weeks for conventional radiation therapy. There is no minimum duration of intervention for surgery or radiosurgery.

Minimum duration of follow‐up

Minimal duration of follow‐up will be 12 weeks.

We will define any follow‐up period going beyond the original time frame for the primary outcome measure as specified in the power calculation of the studies protocol as an extended follow‐up period (also called ‘open‐label extension study') (Buch 2011; Megan 2012).

Summary of specific exclusion criteria

We will exclude studies of the following category of participants.

• Patients less than 18 years of age;

• patients with non‐GH‐secreting pituitary tumours.

Types of outcome measures

We will not exclude a study if it fails to report one or several of our primary or secondary outcome measures. If none of our primary or secondary outcomes is reported in the study, we will not include the study but will provide some basic information in the ‘Characteristics of awaiting classification' table.

We will extract the following outcomes, using the methods and time points specified below.

Primary outcomes

• Disease‐related complications;

• disease recurrence;

• adverse events.

Secondary outcomes

• All‐cause mortality;

• health‐related quality of life;

• biochemical remission;

• change in absolute tumour size;

• surgery for recurrent or persistent disease;

• non‐surgical therapy for recurrent or persistent disease;

• socioeconomic effects.

Method of outcome measurement

• Disease‐related complications: including diabetes, hypertension, colon neoplasia, and cardiac disease;

• disease recurrence: defined as symptomatic tumour growth presenting as overproduction of GH or change in tumour size on MRI following initial treatment and disease control;

• adverse events: related to surgical, pharmacological and radiation therapy (e.g. vascular complications, cerebrospinal fluid leak, meningitis, diabetes insipidus, pituitary insufficiency, gastrointestinal dysfunction, hepatobiliary dysfunction, secondary tumours, optic neuritis death);

• all‐cause mortality: defined as death from any cause;

• health‐related quality of life: evaluated by a validated instruments such as the Short Form 36 (SF‐36);

• biochemical remission: defined as participants achieving remission based on a biochemical target goal of an age‐normalised serum IGF‐1 or a random GH < 1.0 μg/L value;

• change in absolute tumour size: defined as change in maximal tumour diameter and volume on MRI from baseline;

• surgery for recurrent or persistent disease: defined as transsphenoidal reoperation of recurrent (as defined above) or persistent (defined as residual symptomatic disease 12 weeks following the initiation of therapy) pituitary adenoma;

• non‐surgical therapy for recurrent or persistent disease: defined as pharmacotherapy or radiotherapy for recurrent or persistent pituitary adenoma;

• socioeconomic effects: such as direct costs defined as admission/readmission rates, average length of stay, visits to general practitioner, accident/emergency visits; medication consumption; indirect costs defined as resources lost due to illness by the participant or their family member and measured at 30 days.

Timing of outcome measurement

• Disease‐related complications, disease recurrence, biochemical remission, change in absolute tumour size: evaluated at 12 week, 1 year, and 5 year time points;

• adverse events, all‐cause mortality, surgery for recurrent or persistent disease, non‐surgical therapy for recurrent or persistent disease: any time after participants were randomized to interventions groups;

• health‐related quality of life, socioeconomic effects: measured at 30 days postintervention and thereafter.

Search methods for identification of studies

Electronic searches

We will search the following sources from the inception of each database to the specified date and will place no restrictions on the language of publication.

• Cochrane Central Register of Controlled Trials (CENTRAL) via the Cochrane Register of Studies Online (CRSO);

• MEDLINE Ovid (Epub Ahead of Print, In‐Process & Other Non‐Indexed Citations, Ovid MEDLINE Daily and Ovid MEDLINE; from 1946 onwards);

• ClinicalTrials.gov (www.clinicaltrials.gov);

• World Health Organization International Clinical Trials Registry Platform (WHO ICTRP) (www.who.int/trialsearch/).

For detailed search strategies, see Appendix 1. We will not include Embase in our search, as RCTs indexed in Embase are now prospectively added to CENTRAL via a highly sensitive screening process (Cochrane 2018). We will continuously apply an email alert service for MEDLINE via OvidSP to identify newly published studies using the search strategy detailed in Appendix 1.

Searching other resources

We will attempt to identify other potentially eligible studies or ancillary publications by searching the reference lists of included studies, systematic reviews, meta‐analyses, and health technology assessment reports. We will also contact the authors of included studies to obtain additional information on the retrieved studies and establish whether we may have missed further trials.

We will not use abstracts or conference proceedings for data extraction unless full data are available from study authors because this information source does not fulfil the CONSORT requirements which consist of "an evidence‐based, minimum set of recommendations for reporting randomized trials" (CONSORT 2018; Scherer 2018). We will present information on abstracts or conference proceedings in the ‘Characteristics of studies awaiting classification' table. We define grey literature as records detected in ClinicalTrials.gov or the WHO ICTRP.

Data collection and analysis

Selection of studies

Two review authors (LC, JQ) will independently screen the abstract, title, or both, of every record we retrieve in the literature searches, to determine which studies we should assess further. We will obtain the full‐text of all potentially relevant records. We will resolve disagreements through consensus or by recourse to a third review author (MD). If we cannot resolve a disagreement, we will categorise the study as 'awaiting classification' and will contact the study authors for clarification. We will present an adapted PRISMA flow diagram to show the process of study selection (Liberati 2009). We will list all articles excluded after full‐text assessment in a ‘Characteristics of excluded studies' table and will provide the reasons for exclusion.

Data extraction and management

For studies that fulfil our inclusion criteria, two review authors (LC, JQ) will independently extract key information on participants, interventions and comparators. We will describe interventions according to the ‘template for intervention description and replication' (TIDieR) checklist (Hoffmann 2014; Hoffmann 2017).

We will report data on efficacy outcomes and adverse events using standardised data extraction sheets from the CMED Group. We will resolve any disagreements by discussion or, if required, by consulting a third review author (MD).

We will provide information, including the stuy identifier for potentially relevant ongoing trials in the ‘Characteristics of ongoing trials' table and in a joint appendix entitled ‘Matrix of study endpoint (publications and trial documents)'. We will attempt to find the protocol for each included study and will report in a joint appendix the primary, secondary, and other outcomes from these protocols alongside the data from the study publications.

We will email all authors of included studies to enquire whether they would be willing to answer questions regarding their studies. We will present the results of this survey in an appendix. We will thereafter seek relevant missing information on the study from the primary study author(s), if required.

Dealing with duplicate and companion publications

In the event of duplicate publications, companion documents, or multiple reports of a primary study, we will maximise the information yield by collating all available data, and we will use the most complete data set aggregated across all known publications. We will list duplicate publications, companion documents, multiple reports of a primary study, and trial documents of included trials (such as trial registry information) as secondary references under the study ID of the included study. Furthermore, we will list duplicate publications, companion documents, multiple reports of a study, and trial documents of excluded trials (such as trial registry information) as secondary references under the study ID of the excluded study.

Data from clinical trials registers

If data from included trials are available as study results in clinical trials registers, such as ClinicalTrials.gov or similar sources, we will make full use of this information and will extract the data. If there is also a full publication of the study, we will collate and critically appraise all available data. If an included study is marked as a completed study in a clinical trial register but no additional information (study results, publication, or both) is available, we will add this trial to the ‘Characteristics of studies awaiting classification' table.

Assessment of risk of bias in included studies

Two review authors (LC, JQ) will independently assess the risk of bias for each included study. We will resolve any disagreements by consensus or by consulting a third review author (SK). In the case of disagreement, we will consult the remainder of the review author team and make a judgment based on consensus. If adequate information is unavailable from the publications, study protocols, or other sources, we will contact the study authors for more details and to request missing data on ‘Risk of bias' items.

We will use the Cochrane ‘Risk of bias' assessment tool (Higgins 2019b), assigning assessments of low, high, or unclear risk of bias (for details, see Appendix 2; Appendix 3). We will evaluate individual bias items as described in the Cochrane Handbook for Systematic Reviews of Interventions, according to the criteria and associated categorisations contained therein (Higgins 2019b).

Summary assessment of risk of bias

We will present a ‘Risk of bias' graph and a ‘Risk of bias' summary figure.

We will distinguish between self‐reported and investigator‐assessed and adjudicated outcome measures.

We will consider the following self‐reported outcomes:

• adverse events;

• health‐related quality of life.

We will consider the following outcomes to be investigator‐assessed:

• disease‐related complications;

• disease recurrence;

• adverse events;

• all‐cause mortality;

• biochemical remission;

• change in absolute tumour size;

• surgery for recurrent or persistent disease;

• non‐surgical therapy for recurrent or persistent disease;

• socioeconomic effects.

Risk of bias for a study across outcomes

Some ‘Risk of bias' domains, such as selection bias (sequence generation and allocation sequence concealment), affect the risk of bias across all outcome measures in a trial. In case of high risk of selection bias, we will mark all endpoints investigated in the associated study as being at high risk. Otherwise, we will not perform a summary assessment of the risk of bias across all outcomes for a study.

Risk of bias for an outcome within a study and across domains

We will assess the risk of bias for an outcome measure by including all entries relevant to that outcome (i.e. both study‐level entries and outcome‐specific entries). We consider low risk of bias to denote a low risk of bias for all key domains, unclear risk to denote an unclear risk of bias for one or more key domains, and high risk to denote a high risk of bias for one or more key domains.

Risk of bias for an outcome across studies and across domains

To facilitate our assessment of the certainty of evidence for key outcomes, we will assess risk of bias across studies and domains for the outcomes included in the ‘Summary of findings' tables. We will define the evidence as being at low risk of bias when most information comes from studies at low risk of bias, unclear risk of bias when most information comes from studies at low or unclear risk of bias, and high risk of bias when a sufficient proportion of information comes from studies at high risk of bias.

Measures of treatment effect

When at least two included studies are available for a comparison of a given outcome, we will try to express dichotomous data as a risk ratio (RR) or an OR with 95% confidence intervals (CIs). For continuous outcomes measured on the same scale (e.g. weight loss in kg) we will estimate the intervention effect using the mean difference (MD) with 95% CIs. For continuous outcomes that measure the same underlying concept (e.g. health‐related quality of life) but use different measurement scales, we will calculate the standardised mean difference (SMD). We will express time‐to‐event data as a hazard ratio (HR) with 95% CIs.

Unit of analysis issues

We will take into account the level at which randomization occurred, such as cluster‐randomized trials, and multiple observations for the same outcome. If more than one comparison from the same study is eligible for inclusion in the same meta‐analysis, we will either combine groups to create a single pair‐wise comparison, or we will appropriately reduce the sample size so that the same participants do not contribute data to the meta‐analysis more than once (splitting the ‘shared' group into two or more groups). Although the latter approach offers some solution for adjusting the precision of the comparison, it does not account for correlation arising from inclusion of the same set of participants in multiple comparisons (Higgins 2019a).

We will attempt to re‐analyse cluster‐RCTs that have not appropriately adjusted for potential clustering of participants within clusters in their analyses. Variance of the intervention effects will be inflated by a design effect. Calculation of a design effect involves estimation of an intracluster correlation coefficient (ICC). We will obtain estimates of ICCs by contacting trial authors, or by imputing ICC values using either estimates from other included studies that report ICCs or external estimates from empirical research (e.g. Bell 2013). We plan to examine the impact of clustering by performing sensitivity analyses.

Dealing with missing data

If possible, we will obtain missing data from the authors of included studies. We will carefully evaluate important numerical data such as screened, randomly assigned participants, as well as intention‐to‐treat and as‐treated and per‐protocol populations. We will investigate attrition rates (e.g. dropouts, losses to follow‐up, withdrawals), and we will critically appraise issues concerning missing data and use of imputation methods (e.g. last observation carried forward).

For studies in which the standard deviation (SD) of the outcome is not available at follow‐up or we cannot re‐create it, we will standardise by the mean of the pooled baseline SD from studies that reported this information. When included studies do not report means and SDs for outcomes, and we do not receive requested information from study authors, we will impute these values by estimating the mean and the variance from the median, the range, and the size of the sample (Hozo 2005). We will investigate the impact of imputation on meta‐analyses by performing sensitivity analyses, and we will report for every outcome which studies had imputed SDs.

Assessment of heterogeneity

In the event of substantial clinical or methodological heterogeneity, we will not report study results as the pooled effect estimate in a meta‐analysis.

We will identify heterogeneity (inconsistency) by visually inspecting the forest plots and by using a standard Chi² test with a significance level of α = 0.1 (Deeks 2019). In view of the low power of this test, we will— also consider the I² statistic — which quantifies inconsistency across studies — to assess the impact of heterogeneity on the meta‐analysis (Higgins 2002; Higgins 2003). When we identify heterogeneity, we will attempt to determine possible reasons for this by examining individual characteristics of the study subgroups.

Assessment of reporting biases

If we include 10 or more studies that investigate a particular outcome, we will use funnel plots to assess small‐study effects. Several explanations may account for funnel plot asymmetry, including true heterogeneity of effect with respect to study size, poor methodological design (and hence bias of small studies), and selective non‐reporting (Kirkham 2010). Therefore we will interpret the results carefully (Sterne 2011).

Data synthesis

We plan to undertake (or display) a meta‐analysis only if we judge the participants, interventions, comparisons, and outcomes to be sufficiently similar to ensure a result that is clinically meaningful. Unless good evidence shows homogeneous effects across studies of different methodological quality, we will primarily summarise data that are low risk of bias using a random‐effects model (Wood 2008). We will interpret random‐effects meta‐analyses with due consideration for the whole distribution of effects and will present a prediction interval (Borenstein 2017a; Borenstein 2017b; Higgins 2009). A prediction interval requires at least three studies to be calculated and specifies a predicted range for the true treatment effect in an individual study (Riley 2011). For rare events (such as event rates below 1%), we will use the Peto odds ratio method, provided there is no substantial imbalance between intervention and comparator group sizes, and intervention effects are not exceptionally large. In addition, we will perform statistical analyses according to the statistical guidelines presented in the Cochrane Handbook for Systematic Reviews of Interventions (Deeks 2019).

Subgroup analysis and investigation of heterogeneity

We expect the following characteristics to introduce clinical heterogeneity, and we plan to carry out the following subgroup analyses for these, including investigation of interactions (Altman 2003):

• primary GH‐secreting pituitary adenoma (in treatment naive people) versus recurrent (defined as new intrasellar disease following initial treatment and disease control) or persistent (defined as residual symptomatic disease 12 weeks following the initiation of therapy) GH‐secreting pituitary adenoma;

• type and size of GH‐secreting pituitary adenoma (microadenoma, macroadenoma);

• immunohistochemistry (densely‐granulated pattern, sparsely‐granulated pattern).

Sensitivity analysis

When applicable, we plan to explore the influence of important factors on effect sizes, by performing sensitivity analyses in which we restrict the analyses to the following.

• Published studies;

• studies with low risk of bias, as specified in the Assessment of risk of bias in included studies section

• very long or large studies to establish the extent to which they dominate the results.

We will use of the following filters, if applicable: diagnostic criteria, imputation used, language of publication (English versus other languages), source of funding (industry versus other), or country (depending on data).

We will also test the robustness of results by repeating the analyses using different measures of effect size (i.e. RR, OR, etc.) and different statistical models (fixed‐effect and random‐effects models).

Certainty of the evidence

We will present the overall certainty of the evidence for each outcome specified below, according to the GRADE approach, which takes into account issues related not only to internal validity (risk of bias, inconsistency, imprecision, publication bias) and external validity (such as directness of results). Two review authors (LC, JQ) will independently rate the certainty of the evidence for each outcome. We will resoslve any differences in assessment by discussion or by consulting with a third review author (SK).

We will include an appendix entitled ‘Checklist to aid consistency and reproducibility of GRADE assessments', to help with standardisation of the ‘Summary of findings' tables (Meader 2014). Alternatively, we will use GRADEpro GDT software and will present evidence profile tables as an appendix (GRADEproGDT 2015). If meta‐analysis is not possible, we will present the results in a narrative format in the ‘Summary of findings' table. We will justify all decisions to downgrade the quality of studies by using footnotes, and we will make comments to aid the reader's understanding of the Cochrane Review when necessary.

‘Summary of findings' table

We will present a summary of the evidence in a ‘Summary of findings' table. This will provide key information about the best estimate of the magnitude of effect, in relative terms and as absolute differences for each relevant comparison of alternative management strategies; the numbers of participants and studies addressing each important outcome; and a rating of overall confidence in effect estimates for each outcome. We will create the ‘Summary of findings' table using the methods described in the Cochrane Handbook for Systematic Reviews of Interventions (Schünemann 2019), along with Review Manager 5 software (RevMan 2014). We plan to present in the ‘Summary of findings' table the following interventions and comparators:

• surgery (endoscopic or microscopic transsphenoidal surgery) alone compared with:

  • pharmacological therapy (dopamine agonists, somatostatin analogs, or GH receptor blocker) alone;

  • radiation therapy (radiosurgery, stereotactic radiotherapy, or conventional radiation therapy) alone;

  • combination therapy:

    • preoperative pharmacological therapy (maximum six months) and surgery; or

    • surgery and postoperative pharmacological therapy; or

    • surgery and postoperative radiation therapy; or

    • pretreatment with pharmacological therapy followed by radiation therapy; or

    • radiation therapy followed by pharmacological therapy;

• pharmacological therapy alone compared with:

  • radiation therapy;

  • combination therapy:

    • preoperative pharmacological therapy (maximum six months) and surgery;

    • surgery and postoperative pharmacological therapy; or

    • surgery and postoperative radiation therapy; or

    • pretreatment with pharmacological therapy followed by radiation therapy; or

    • radiation therapy followed by pharmacological therapy;

• surgery and postoperative medical therapy compared with:

  • preoperative pharmacological therapy and surgery; or

  • surgery and postoperative radiation therapy; or

  • pretreatment with pharmacological therapy followed by radiation therapy; or

  • radiation therapy followed by pharmacological therapy;

• pre‐operative medical therapy (maximum six months) and surgery compared with:

  • surgery and postoperative radiation therapy; or

  • radiation therapy followed by pharmacological therapy.

We will report the following outcomes, listed according to priority.

• Disease‐related complications;

• disease recurrence;

• adverse events;

• all‐cause mortality;

• surgery for recurrent or persistent disease;

• non‐surgical therapy for recurrent or persistent disease;

• health‐related quality of life.

Notes

We have based parts of the Methods, as well as Appendix 1, Appendix 2, and Appendix 3 of this Cochrane protocol, on a standard template established by the CMED Group.

Appendices

Appendix 1. Search strategies

MEDLINE (Ovid)

1. Acromegaly/ 2. Pituitary Neoplasms/ 3. Growth Hormone‐Secreting Pituitary Adenoma/ 4. (pituitary adj6 (adenoma* or neoplasm* or tumor* or tumour* or macroadenoma* or microadenoma)).tw. 5. (growth hormone adj3 (adenoma* or neoplasm* or tumor* or tumour* or macroadenoma* or microadenoma)).tw. 6. (GH adj3 (adenoma* or neoplasm* or tumor* or tumour* or macroadenoma* or microadenoma)).tw. 7. (acromegal* or pseudoacromegal*).tw. 8. (somatotroph adenoma* or somatotrophinoma* or somatotropinoma*).tw. 9. or/1‐8 [10‐20: Cochrane Handbook RCT filter ‐ sensitivity max. version] 10. randomized controlled trial.pt. 11. controlled clinical trial.pt. 12. randomi?ed.ab. 13. placebo.ab. 14. drug therapy.fs. 15. randomly.ab. 16. trial.ab. 17. groups.ab. 18. or/10‐17 19. exp animals/ not humans/ 20. 18 not 19 21. 9 and 20

Cochrane Central Register of Controlled Trials (Cochrane Register of Studies Online)

1. MESH DESCRIPTOR Acromegaly 2. MESH DESCRIPTOR Pituitary Neoplasms 3. MESH DESCRIPTOR Growth Hormone‐Secreting Pituitary Adenoma 4. (pituitary ADJ6 (adenoma* or neoplasm* or tumor* or tumour* or macroadenoma* or microadenoma)):TI,AB,KY 5. (growth hormone ADJ3 (adenoma* or neoplasm* or tumor* or tumour* or macroadenoma* or microadenoma)):TI,AB,KY 6. (GH ADJ3 (adenoma* or neoplasm* or tumor* or tumour* or macroadenoma* or microadenoma)):TI,AB,KY 7. (acromegal* or pseudoacromegal*):TI,AB,KY 8. (somatotroph adenoma* or somatotrophinoma* or somatotropinoma*):TI,AB,KY 9. #1 OR #2 OR #3 OR #4 OR #5 OR #6 OR #7 OR #8

WHO ICTRP (Standard search)

acromegal* OR pseudoacromegal* OR pituitary AND neoplasm* OR pituitary AND adenoma* OR pituitary AND tumor* OR pituitary AND tumour* OR pituitary AND macroadenoma* OR pituitary AND microadenoma* OR pituitary AND neoplasm* OR pituitary AND neoplasm* OR pituitary AND adenoma* OR pituitary AND tumor* OR pituitary AND tumour* OR pituitary AND macroadenoma* OR pituitary AND microadenoma* OR pituitary AND neoplasm* OR somatotroph AND adenoma* OR somatotrophinoma* OR somatotropinoma*

ClinicalTrials.gov (Expert search)

acromegaly OR acromegalic OR pseudoacromegaly OR (pituitary AND (neoplasm OR neoplasms OR tumor OR tumour OR tumors OR tumours OR macroadenoma OR macroadenomas OR microadenoma OR microadenomas)) OR ("growth hormone" AND (neoplasm OR neoplasms OR tumor OR tumour OR tumors OR tumours OR macroadenoma OR macroadenomas OR microadenoma OR microadenomas)) OR "somatotroph adenoma" OR "somatotroph adenomas" OR somatotrophinoma OR somatotrophinomas OR somatotropinoma OR somatotropinomas

Appendix 2. Selection bias decisions

Selection bias decisions for studies that reported unadjusted analyses: comparison of results obtained using method details alone versus results obtained using method details and study baseline informationa
Reported randomization and allocation concealment methods	‘Risk of bias' judgement using methods reporting	Information gained from study characteristics data	‘Risk of bias' using baseline information and methods reporting
Unclear methods	Unclear risk	Baseline imbalances present for important prognostic variable(s)	High risk
		Groups appear similar at baseline for all important prognostic variables	Low risk
		Limited or no baseline details	Unclear risk
Would generate a truly random sample, with robust allocation concealment	Low risk	Baseline imbalances present for important prognostic variable(s)	Unclear riskb
		Groups appear similar at baseline for all important prognostic variables	Low risk
		Limited baseline details, showing balance in some important prognostic variablesc	Low risk
		No baseline details	Unclear risk
Sequence is not truly randomized or allocation concealment is inadequate	High risk	Baseline imbalances present for important prognostic variable(s)	High risk
		Groups appear similar at baseline for all important prognostic variables	Low risk
		Limited baseline details, showing balance in some important prognostic variablesc	Unclear risk
		No baseline details	High risk
aTaken from Corbett 2014; judgements highlighted in bold indicate situations in which the addition of baseline assessments would change the judgement about risk of selection bias compared with using methods reporting alone. bImbalance was identified that appears likely to be due to chance. cDetails for the remaining important prognostic variables are not reported.

Appendix 3. ‘Risk of bias' assessment

‘Risk of bias' domains

Random sequence generation (selection bias due to inadequate generation of a randomized sequence) For each included study, we will describe the method used to generate the allocation sequence in sufficient detail to allow an assessment of whether it should produce comparable groups. Low risk of bias: study authors achieved sequence generation using computer‐generated random numbers or a random numbers table. Drawing of lots, tossing a coin, shuffling cards or envelopes, and throwing dice are adequate if an independent person performed this who was not otherwise involved in the study. We will consider the use of the minimisation technique as equivalent to being random. Unclear risk of bias: insufficient information about the sequence generation process. High risk of bias: the sequence generation method was non‐random or quasi‐random (e.g. sequence generated by odd or even date of birth; sequence generated by some rule based on date (or day) of admission; sequence generated by some rule based on hospital or clinic record number; allocation by judgment of the clinician; allocation by preference of the participant; allocation based on the results of a laboratory test or a series of tests; or allocation by availability of the intervention). Allocation concealment (selection bias due to inadequate concealment of allocation prior to assignment) We will describe for each included study the method used to conceal allocation to interventions prior to assignment and we will assess whether intervention allocation could have been foreseen in advance of or during recruitment or changed after assignment. Low risk of bias: central allocation (including telephone, interactive voice‐recorder, internet‐based and pharmacy‐controlled randomization); sequentially numbered drug containers of identical appearance; sequentially numbered, opaque, sealed envelopes. Unclear risk of bias: insufficient information about the allocation concealment. High risk of bias: used an open random allocation schedule (e.g. a list of random numbers); assignment envelopes used without appropriate safeguards; alternation or rotation; date of birth; case record number; any other explicitly unconcealed procedure. We will also evaluate study baseline data to incorporate assessment of baseline imbalance into the ‘Risk of bias' judgement for selection bias (Corbett 2014). Chance imbalances may also affect judgements on the risk of attrition bias. In the case of unadjusted analyses, we will distinguish between studies that we rate as being at low risk of bias on the basis of both randomization methods and baseline similarity, and studies that we judge as being at low risk of bias on the basis of baseline similarity alone (Corbett 2014). We will reclassify judgements of unclear, low, or high risk of selection bias as specified in Appendix 3. Blinding of participants and study personnel (performance bias due to knowledge of the allocated interventions by participants and personnel during the study) We will evaluate the risk of detection bias separately for each outcome (Hróbjartsson 2013). We will note whether endpoints were self‐reported, investigator‐assessed, or adjudicated outcome measures (see below). Low risk of bias: blinding of participants and key study personnel was ensured, and it was unlikely that the blinding could have been broken; no blinding or incomplete blinding, but we judge that the outcome is unlikely to have been influenced by lack of blinding. Unclear risk of bias: insufficient information about the blinding of participants and study personnel; the study does not address this outcome. High risk of bias: no blinding or incomplete blinding, and the outcome is likely to have been influenced by lack of blinding; blinding of study participants and key personnel attempted, but likely that the blinding could have been broken, and the outcome is likely to be influenced by lack of blinding. Blinding of outcome assessment (detection bias due to knowledge of the allocated interventions by outcome assessment) We will evaluate the risk of detection bias separately for each outcome (Hróbjartsson 2013). We will note whether endpoints were self‐reported, investigator‐assessed, or adjudicated outcome measures (see below). Low risk of bias: blinding of outcome assessment is ensured, and it is unlikely that the blinding could have been broken; no blinding of outcome assessment, but we judge that the outcome measurement is unlikely to have been influenced by lack of blinding. Unclear risk of bias: insufficient information about the blinding of outcome assessors; the study did not address this outcome. High risk of bias: no blinding of outcome assessment, and the outcome measurement was likely to have been influenced by lack of blinding; blinding of outcome assessment, but likely that the blinding could have been broken, and the outcome measurement was likely to be influenced by lack of blinding. Incomplete outcome data (attrition bias due to quantity, nature, or handling of incomplete outcome data) For each included study or each outcome, or both, we will describe the completeness of data, including attrition and exclusions from the analyses. We will state whether the study reported attrition and exclusions, and we will report the number of participants included in the analysis at each stage (compared with the number of randomized participants per intervention/comparator groups). We will also note if the study reported the reasons for attrition or exclusion, and whether missing data were balanced across groups or were related to outcomes. We will consider the implications of missing outcome data per outcome such as high dropout rates (e.g. above 15%) or disparate attrition rates (e.g. difference of 10% or more between study arms). Low risk of bias: no missing outcome data; reasons for missing outcome data unlikely to be related to true outcome (for survival data, censoring unlikely to introduce bias); missing outcome data balanced in numbers across intervention groups, with similar reasons for missing data across groups; for dichotomous outcome data, the proportion of missing outcomes compared with observed event risk was not enough to have a clinically relevant impact on the intervention effect estimate; for continuous outcome data, plausible effect size (mean difference or standardised mean difference) among missing outcomes was not enough to have a clinically relevant impact on observed effect size; appropriate methods, such as multiple imputation, were used to handle missing data. Unclear risk of bias: insufficient information to assess whether missing data in combination with the method used to handle missing data were likely to induce bias; the study did not address this outcome. High risk of bias: reason for missing outcome data was likely to be related to true outcome, with either imbalance in numbers or reasons for missing data across intervention groups; for dichotomous outcome data, the proportion of missing outcomes compared with observed event risk enough to induce clinically relevant bias in the intervention effect estimate; for continuous outcome data, plausible effect size (mean difference or standardised mean difference) among missing outcomes enough to induce clinically relevant bias in observed effect size; ‘as‐treated' or similar analysis done with substantial departure of the intervention received from that assigned at randomization; potentially inappropriate application of simple imputation. Selective reporting (reporting bias due to selective outcome reporting) We will assess outcome reporting bias by integrating the results of the appendix ‘Matrix of study endpoints (publications and trial documents)' (Boutron 2014; Jones 2015; Mathieu 2009), with those of the appendix ‘High risk of outcome reporting bias according to the Outcome Reporting Bias In Trials (ORBIT) classification' (Kirkham 2010). This analysis will form the basis for the judgement of selective reporting. Low risk of bias: the study protocol was available and all the study's prespecified (primary and secondary) outcomes that were of interest to this review were reported in the prespecified way; the study protocol was unavailable, but it was clear that the published reports included all expected outcomes (ORBIT classification). Unclear risk of bias: insufficient information about selective reporting. High risk of bias: not all the study's prespecified primary outcomes were reported; one or more primary outcomes were reported using measurements, analysis methods, or subsets of the data (e.g. subscales) that were not prespecified; one or more reported primary outcomes were not prespecified (unless clear justification for their reporting was provided, such as an unexpected adverse effect); one or more outcomes of interest in the Cochrane Review were reported incompletely so that we cannot enter them into a meta‐analysis; the study report failed to include results for a key outcome that we would expect to have been reported for such a study (ORBIT classification). Other bias Low risk of bias: the study appears to be free from other sources of bias. Unclear risk of bias: information was insufficient to assess whether an important risk of bias existed; insufficient rationale or evidence that an identified problem introduced bias. High risk of bias: the study had a potential source of bias related to the specific study design used; the study was claimed to be fraudulent; or the study had some other serious problem.

Contributions of authors

All review authors contributed to, read, and approved the final protocol draft.

Sources of support

Internal sources

• None, Other.

External sources

• None, Other.

Declarations of interest

Lisa Caulley (LC): is currently supported by a Canadian Institutes of Health Research Frederick Banting and Charles Best Canada Graduate Scholarships Doctoral Award.
 Jason G Quinn (JQ): none known.
 Mary‐Anne Doyle (MD): none known.
 Fahad Alkherayf (FA): none known.
 Shaun Kilty (SK): none known.
 M G Myriam Hunink (MG): receives royalties from Cambridge University Press for a textbook on decision making; and received reimbursement for travel and lodging from the European Society of Radiology and the European Institute for Biomedical Imaging Research.

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