Local anesthetics for intrauterine device placement

Objectives

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

To examine the effectiveness of local anesthetics for routine intrauterine device (IUD) placement on patient (e.g. pain, side effects, satisfaction) and provider outcomes (e.g. ease of placement, need for adjunctive placement measures, placement success) compared with placebo or no treatment.

Background

We acknowledge that individuals who use intrauterine devices (IUDs) may not identify as women, and we have endeavored to use gender‐inclusive language throughout this protocol. When reporting on individual studies that identified participants as 'women' or 'female', we have kept this language to accurately describe the study as it was reported.

Description of the condition

IUDs have high contraceptive effectiveness, user satisfaction, and continuation rates (Peipert 2011; Trussell 2018). IUDs are generally safe for individuals to use, including adolescents and nulliparous individuals, based on the World Health Organization Medical Eligibility Criteria for Contraceptive Use (World Health Organization 2015) and the US Medical Eligibility Criteria for Contraceptive Use (Curtis 2016). Worldwide, IUDs are the most commonly used reversible method of contraception (Buhling 2014a; Dean 2018), although IUD use ranges from less than 2% in some countries to greater than 40% in other countries (Buhling 2014a). IUD use in the US has increased in recent years. Based on data from the National Survey of Family Growth, current IUD use among US women of reproductive age was 3.5% in 2006 to 2010 (women aged 15 to 44 years), 7.9% in 2015 to 2017 (women aged 15 to 49 years), and 8.4% in 2017 to 2019 (women aged 15 to 49 years) (Daniels 2018; Daniels 2020; Jones 2012). Prevalence of IUD use in the US in 2017 to 2019 remained lower than female sterilization (18.1%) and oral contraceptive pills (14.0%) but was the same as male condoms (8.4%) (Daniels 2020). 

While first attempts at placing IUDs are usually successful (Teal 2015), some placements may be painful for the patient or technically difficult for the provider. During an IUD placement, pain can occur at different steps of the procedure: application of an instrument to the cervix to stabilize the uterus, uterine sounding, IUD insertion through the cervix, and IUD deployment in the uterus. Patients have different pain thresholds and may experience different levels of pain during IUD placement. Risk factors for greater pain with in‐office transcervical gynecological procedures, including IUD placement, have been described and include being nulliparous, being postmenopausal, having a history of dysmenorrhea, suffering from anxiety, and anticipating pain (Chaves 2021; Dina 2018; Hunter 2020; Ireland 2016). The potential for or fear of pain with IUD placement has been reported as a barrier to IUD initiation and a reason why patients have chosen other contraceptive methods (Gomez 2015; Hubacher 2015; Potter 2014).

Another barrier to IUD use is provider concern about potential technical difficulty with placement, particularly for certain patient populations including adolescents and nulliparous individuals (Buhling 2014b; Daniele 2017; Elkhateeb 2020). However, one study found that IUDs can be placed in nulliparous adolescents and young individuals aged 18 to 24 years with high (greater than 95%) and similar success rates as their parous counterparts without the use of adjunctive measures, by both physicians and advanced practice clinicians (Teal 2015). Another study similarly found no difference in IUD placement success rates between nulliparous and parous individuals (mean age of 23 years) receiving an IUD for emergency contraception without the use of adjunctive measures, placed by nurse practitioners in a community setting, although IUD placement success rates for both nulliparous and parous individuals were lower than expected (80% and 86%, respectively) (Dermish 2013). However, after participation in a training program focused on managing difficult IUD placements (the program included a two‐hour didactic session by a family planning expert, hands‐on training, and proctored IUD placements with cervical anesthesia or dilation), clinicians’ rate of successful IUD placements improved significantly (greater than 95% for both nulliparous and parous individuals) (Dermish 2016). Yet another study did find differences in IUD placement success rates between nulliparous individuals (89%), individuals with prior cesarean delivery only (89%), and parous individuals (98%), among a sample of individuals having an IUD placed by a physician in the primary care setting (61% were aged over 35 years and 64% used local anesthesia) (Harvey 2012). This study also found that practitioners placing fewer than 100 IUDs over the 12‐month study period more frequently rated IUD placements as difficult (Harvey 2012). 

Description of the intervention

Local anesthetics relieve pain by transiently inhibiting nerve function of nearby tissue where applied and reduction of pain can be achieved without sedation (American Society of Anesthesiologists 2022; Butterworth 2018). Local anesthetics commonly used in gynecological procedures include ester and amide anesthetics (Allen 2018). Esters, such as procaine, 2‐cholorprocaine, and tetracaine, are hydrolyzed by plasma pseudocholinesterase and are associated with higher risk of allergic reaction, although allergic reactions are still considered rare (Allen 2018; Butterworth 2018). Amides, such as lidocaine and bupivacaine, are metabolized by the liver and more commonly used (Allen 2018). In general, side effects of local anesthetics can include dizziness, tinnitus, circumoral numbness, tongue paresthesia, blurred vision, and feelings of restlessness, nervousness, or agitation (Butterworth 2018). Local anesthetics relax smooth muscle, cause dilation of arteries, and can depress myocardial automaticity at high blood concentrations of local anesthetics (Butterworth 2018). Therefore, while severe adverse events are rare, they can include seizures, respiratory failure, and cardiac arrest (Butterworth 2018). Risk of amide toxicity may be increased in patients with decreased hepatic function or liver blood flow (Butterworth 2018).

How the intervention might work

The use of local anesthetics may reduce pain during IUD placement.

Why it is important to do this review

Identifying effective approaches to reduce patient pain and improve provider ease of IUD placement may reduce barriers to IUD access and increase patient access to comprehensive contraceptive options, a key strategy to promote reproductive autonomy and equitable contraceptive care (Holt 2020). The information may be used by guideline groups that publish evidence‐based contraception guidance like the World Health Organization (WHO), the US Centers for Disease Control and Prevention (CDC), the UK Faculty of Sexual and Reproductive Healthcare (FSRH), and the Society of Obstetricians and Gynaecologists of Canada (SOGC).

Objectives

To examine the effectiveness of local anesthetics for routine intrauterine device (IUD) placement on patient (e.g. pain, side effects, satisfaction) and provider outcomes (e.g. ease of placement, need for adjunctive placement measures, placement success) compared with placebo or no treatment.

Methods

Criteria for considering studies for this review

Types of studies

We will include parallel randomized controlled trials (RCTs) including those randomized at the individual or cluster level and will not include cross‐over trials because this is not feasible for studies of the intervention evaluated in this review. We will exclude non‐randomized trials, observational studies, cross‐sectional studies, case series, review articles, editorials, letters, and conference abstracts. We will include peer‐reviewed articles published in any language.

Types of participants

Participants of interest will be patients receiving an interval intrauterine device (IUD) (i.e. placement not in the postabortion or postpartum period). We will include studies that examine placement of currently available levonorgestrel (LNG)‐releasing IUDs, or any copper T IUD, for individuals of any age, of any parity, and for any indication. We will include studies that examine multiple IUD types if most participants received an IUD meeting the above‐mentioned criteria. We will exclude studies of placements occurring exclusively postpartum or postabortion (defined for this review as up to six weeks postpartum and four weeks postabortion). We will include studies that examine mixed placement timing (interval and postpartum or postabortion) if findings are stratified by placement timing.

Types of interventions

We will include studies that directly compare local anesthetics for IUD placement with placebo or no treatment. All routes of administration, doses, and regimens will be included with no limits as long as the local anesthetic is given for IUD placement.

Types of outcome measures

Primary outcomes

• Patient pain with IUD placement (as rated by a visual analog scale or other tool) at specific time points (i.e. during tenaculum placement, during IUD placement, and highest level of pain after placement and before clinic discharge).

• Provider ease of placement (as rated by a visual analog scale or other tool).

• Need for adjunctive placement measures (e.g. cervical dilation, ultrasound guidance, local anesthesia, analgesia).

• Placement success.

Secondary outcomes

• Patient satisfaction with procedure (as rated by a Likert scale or other tool before clinic discharge).

• Local anesthetic side effects occurring before clinic discharge (each of the following will be reported: vomiting, dizziness, tinnitus, circumoral numbness, tongue paresthesia).

• Adverse events occurring before clinic discharge (each of the following will be reported: uterine perforation, vasovagal reaction, cervical laceration, seizure, respiratory failure, cardiac arrest).

Search methods for identification of studies

The Fertility Regulation Group Information Specialist will conduct a search for all published, unpublished, and ongoing studies, without restrictions on language or publication status. The search strategies for each database will be modeled on the search strategy designed for MEDLINE ALL Ovid.

Electronic searches

We will search the following databases from their inception:

• Cochrane Central Register of Controlled Trials (CENTRAL) Ovid EBM Reviews

• MEDLINE ALL Ovid (from 1946 onwards)

• Embase.com (from 1974 onwards)

• World Health Organization Global Index Medicus

• Global Health Ovid (from 1973 onwards)

• Scopus.

We will search the following trials registries:

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

• US National Institutes of Health Ongoing Trials Register ClinicalTrials.gov (www.clinicaltrials.gov).

Searching other resources

We will check the bibliographies of included studies and any relevant systematic reviews identified for further references to relevant studies. We will contact experts/organizations in the field to obtain additional information on relevant studies. If necessary, we will contact authors of included studies for data clarification and further information. We will consider adverse effects described in included studies only.

Data collection and analysis

Selection of studies

We will download all titles and abstracts retrieved by electronic searching to a reference management database and remove duplicates. Three review authors (Lauren Zapata (LZ), Antoinette Nguyen (AN), and Kathryn Curtis (KC)) will independently screen titles and abstracts for inclusion. We will retrieve the full‐text study reports/publication and two review authors (LZ, AN) will independently screen the full text and identify studies for inclusion, and identify and record reasons for exclusion of the ineligible studies. We will resolve any disagreement through discussion or, if required, we will consult a third review author (KC). We will list studies that initially appeared to meet the inclusion criteria but that we later excluded in the 'Characteristics of excluded studies' table. We will collate multiple reports of the same study so that each study rather than each report is the unit of interest in the review. We will record the selection process in sufficient detail to complete a PRISMA flow diagram (Liberati 2009; Page 2021).

Data extraction and management

We will use a standard data collection form for study characteristics and outcome data; we will pilot the form on at least one study in the review. Two review authors (LZ, AN) will independently extract the study characteristics from the included studies. This may include.

• Methods: study design, number of study centers and location, study setting, withdrawals, date of study, follow‐up, approach to adjustment for design effects or confounding.

• Participants: number, mean age, age range, parity, IUD type, inclusion criteria, exclusion criteria, other relevant characteristics.

• Intervention: dose, route, regimen, co‐interventions, comparison, compliance.

• Outcomes: events, means, relative effects, time points reported, adjusted effect estimates and information about the confounders and design effects accounted for, intracluster correlations for studies with clustering.

• Notes: funding for trial, notable conflicts of interest of trial authors, ethical approval.

Two review authors (LZ, AN) will independently extract outcome data from included studies. We will note in the 'Characteristics of included studies' table if outcome data were reported in an unusable way. We will resolve disagreements by consensus or by involving a third review author (KC).

Assessment of risk of bias in included studies

Two review authors (LZ, AN) will independently assess the risk of bias for key outcomes defined in this protocol using the Cochrane RoB2 tool (Sterne 2019) and criteria outlined in Chapter 8 of Version 6.3 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2022a). We will resolve any disagreements by discussion or by involving another author (KC). Our effect of interest will be effect of assignment, also known as the ‘intention to treat.’ The following domains of bias will be assessed using a series of signaling questions.

• Bias arising from the randomization process.

• Bias due to deviations from intended interventions.

• Bias due to missing outcome data.

• Bias in measurement of the outcomes.

• Bias in selection of the reported result.

An additional domain of bias will be assessed for cluster‐randomized trials (i.e. bias arising from identification or recruitment of individual participants within clusters). We will use the variants of RoB2 for cluster‐RCTs if we identify eligible trials with this study design. 

For each outcome, we will use the signaling questions to categorize each domain as low risk of bias, some concerns, or high risk of bias. Answers to the signaling questions will be recorded using software (e.g. RoB2 Excel tool, Covidence) and made available in an online repository. We will summarize the risk of bias judgments across different studies for each of the domains for each prespecified outcome.

For each study, we will derive an overall judgment from the tool, as follows.

• Low risk of bias: we consider the study shows a low risk of bias.

• Some concerns: we expect a few concerns are associated with the study in at least one domain, but it does not warrant categorization as a study with a high risk of bias for any domain. 

• High risk of bias: we consider the study to be at high risk of bias in at least one domain; or we observed a few concerns with multiple domains in the study, such that these concerns significantly lower confidence in the study results.

We will not exclude studies on the grounds of their risk of bias but will clearly report the risk of bias when presenting the results of the studies. When summarizing the evidence on intervention effects for different outcomes, we will consider the risk of bias for the studies that contributed to analyses for that outcome. We will conduct sensitivity analyses to assess whether estimated effects differ when high risk of bias studies are excluded from analyses. 

Measures of treatment effect

We will analyze dichotomous data risk and/or odds ratios with 95% confidence intervals (CIs) and continuous data as mean difference (MD) or standardized mean difference (SMD) with 95% CIs. Outcomes adjusted for confounders or design effects (e.g. clustering) will be reported and, where possible, used for meta‐analysis. We will ensure that we enter data into the analysis with a consistent direction of effect (i.e. reversing the numeric coding of scales if needed).

We will use SMDs when studies use different scales to measure the same outcomes, necessitating the standardization of study results to a uniform scale before they can be combined. The SMD expresses the size of the intervention effect in each study relative to the variability observed in that study, thus studies for which the difference in means is the same proportion of the standard deviation will have the same SMD, regardless of the actual scales used to make the measurements. To interpret the SMD, we will use the Cohen’s effect size rubric where 0.2 represents a small effect, 0.5 a moderate effect, and 0.8 a large effect (Cohen 1988 ). If possible, we will express the study SMDs using a recognizable and standard metric used by some of the included studies or employ other strategies to aid interpretability outlined in Chapter 15 of Version 6.3 of the Cochrane Handbook for Systematic Reviews of Interventions (Schünemann 2022a).

For studies reporting results that are not provided in a format that can be directly entered into meta‐analysis, we will use guidance provided in Chapter 6 of Version 6.3 of the Cochrane Handbook for Systematic Reviews of Interventions to convert the data to the necessary format (Higgins 2022b).

Unit of analysis issues

We will perform the primary analysis per individual randomized. We will abstract information on the study design and unit of analysis for each study, indicating whether clustering of observations is present due to allocation to the intervention at the group level or clustering of individually randomized observations (e.g. patients within clinics). Available statistical information needed to account for the implications of clustering on the estimation of outcome variances will be abstracted, such as design effects or intracluster correlations, and whether the study adjusted results for the correlations in the data. In cases where the study does not account for clustering, we will ensure that appropriate adjustments are made to the effective sample size following guidance provided in Chapter 6 of Version 6.3 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2022b). Where possible, we will derive the intracluster correlation for these adjustments from the trial itself, or from a similar trial. If an appropriate intracluster correlation is unavailable, we will conduct sensitivity analyses to investigate the potential effect of clustering by imputing a range of values of intracluster correlation.

If any trials have multiple arms that are compared against the same control condition and we need to include them in the same meta‐analysis, we will divide the control group numerators and denominators by the number of interventions to be included in the meta‐analysis, to avoid double‐counting observations.

Dealing with missing data

If included studies have missing data for key study characteristics or key outcomes, we will contact investigators or study sponsors to attempt to obtain the missing information. 
We will calculate missing standard deviations or other necessary data using other data from the trial, such as confidence intervals, based on methods outlined in Chapter 6 of Version 6.3 of the Cochrane Handbook for Systematic Reviews of Interventions (Higgins 2022b).

We will report the number of studies that have results missing for the synthesis of each outcome.

We will show all responses and data provided in the 'Characteristics of included studies' table. Where we make any assumptions about missing data, we will report the potential impact in the 'Discussion' section of the review.

Assessment of heterogeneity

We will describe the clinical diversity and methodological variability of the evidence in the review text and will use tables to describe study characteristics, including design features, population characteristics, and intervention details.

To assess statistical heterogeneity, we will visually inspect forest plots and describe the direction and magnitude of effects, and the degree of overlap between confidence intervals. We will also consider the statistics generated in forest plots that measure statistical heterogeneity. We will use the I² statistic to quantify inconsistency among the trials in each analysis. We will also consider the P value from the Chi² test to assess whether this heterogeneity is significant (P < 0.1). If we identify substantial heterogeneity, we will report the finding and explore possible explanatory factors using prespecified subgroup analysis.

We will use a rough guideline to interpret the I² value rather than a simple threshold, and our interpretation will consider an understanding that measures of heterogeneity (I² and Tau²) will be estimated with high uncertainty when the number of studies is small (Deeks 2022):

• 0% to 40%: heterogeneity might not be important;

• 30% to 60%: may represent moderate heterogeneity;^a

• 50% to 90%: may represent substantial heterogeneity;^a

• 75% to 100%: considerable heterogeneity.^a

^aThe importance of the observed value of I² depends on (1) the magnitude and direction of effects, and (2) the strength of evidence for heterogeneity (e.g. P value from the Chi² test, or a confidence interval for I²).

Assessment of reporting biases

If we have enough studies available for meta‐analysis to support a funnel plot (at least 10), we will create and visually inspect the funnel plot and run a formal statistical test for asymmetry, as proposed by Egger and colleagues (Egger 1997). We plan to provide a funnel plot for patient pain with IUD insertion, provider ease of placement, and placement success, data permitting. If we observe funnel plot asymmetry, we will discuss the potential for this to be attributed to small study effects and not just non‐reporting bias. If our review includes few studies eligible for meta‐analysis, the ability to detect publication bias will be largely diminished and we will simply note our inability to rule out possible publication bias or small study effects.

Data synthesis

We will undertake meta‐analyses to estimate pooled effects when the studies report adequate comparable data that can support statistical pooling. When we suspect that data are skewed, based on the reporting of median and interquartile ranges, we will note the skewness and discuss the implication, but will not pool medians with means.

For outcomes with data that cannot be statistically pooled, we will present descriptive forest plots showing the individual study results to illustrate the range of effects reported.

If data are adequate to support meta‐analysis, the analytic approach we take will be based on an evaluation of the clinical and methodological diversity of the included studies, as well as the statistical heterogeneity. For rare outcomes, we will use the Peto odds ratio method. For more common outcomes, we will generate the pooled effect using the DerSimonian and Laird random‐effects estimation technique. We will consider calculating a fixed‐effect estimate using the Mantel‐Haenszel approach if we can assume that the included studies are estimating the same intervention effect, if the intervention effects are relatively consistent in direction and magnitude, and heterogeneity is low. We will also consider the Mantel‐Haenszel approach if there is evidence of potential variation in outcome effects by study size (i.e. small‐study effects). We will discuss the implications and assumptions of the choice of meta‐analysis model if results differ, but the default approach for this topic will be the random‐effects model. We will illustrate each meta‐analysis using a forest plot to display effect estimates and 95% CIs for both individual studies' effects and the pooled effect.

If we cannot summarize the study data quantitatively, we will follow guidance available for synthesis without meta‐analysis outlined in Chapter 12 of Version 6.3 of the Cochrane Handbook for Systematic Reviews of Interventions (McKenzie 2022) and guidance on the reporting of synthesis without meta‐analysis (SWiM) in systematic reviews (Campbell 2020).

Subgroup analysis and investigation of heterogeneity

We will interpret tests for subgroup differences in effects with caution, given the potential for confounding with other study characteristics and the observational nature of the comparisons, as recommended in Chapter 10.11.2 of Version 6.3 of the Cochrane Handbook for Systematic Reviews of Interventions (Deeks 2022). Subgroup analyses with fewer than five studies per category are unlikely to be adequate to ascertain valid difference in effects and will not be highlighted in our results. When adequate data are available to conduct meaningful subgroup analyses, we will evaluate factors that could explain observed statistical heterogeneity. We will conduct a statistical test for interactions with either a simple significance test to investigate differences between two or more subgroups (Borenstein 2013) or use meta‐regression to evaluate potential subgroups differences in outcomes according to the factors described below (Borenstein 2013). We will only use meta‐regression if there are more than 10 studies available for meta‐analysis.

We plan to carry out subgroup analyses that may contribute to heterogeneity in the effects of the intervention. This may include subgroup analyses comparing studies grouped by the following factors:

• nulliparous versus parous (or prior vaginal delivery versus no prior vaginal delivery);

• IUD type (smaller versus larger width of inserter and smaller versus larger IUD size).

If studies do not report stratified results, results may be grouped into categories that reflect most participants or will include a subgroup category of mixed populations. We will use the following outcomes in subgroup analyses if there are enough studies reporting the outcome to support valid subgroup comparisons: patient pain with IUD placement (during IUD placement or highest level of pain after placement and before clinic discharge (whichever measure is most consistently reported)) and provider ease of placement.

Sensitivity analysis

We will perform sensitivity analyses defined a priori to assess the robustness of our conclusions. This will involve restricting the analyses to studies with a low risk of bias, as specified in the section ‘Assessment of risk of bias in included studies.’ Given that there is no formal statistical test that can be used for sensitivity analysis, we will provide informal comparisons between the different ways of estimating the effect under different assumptions. We will report sensitivity analysis results in tables rather than forest plots.

Summary of findings and assessment of the certainty of the evidence

We will evaluate the evidence according to the five GRADE considerations (study limitations, consistency of effect, imprecision, indirectness, and publication bias) to assess the certainty of the body of evidence as it relates to our prespecified outcomes.

We will follow the methods and recommendations described in Chapter 14 of Version 6.3 of the Cochrane Handbook for Systematic Reviews of Interventions (Schünemann 2022b) and will use GRADEpro GDT software.

In the summary of findings table, we will provide results for the following key outcomes.

• Patient pain with IUD placement (as rated by a visual analog scale or other tool) at specific time points (i.e. during tenaculum placement, during IUD placement, and highest level of pain after placement and before clinic discharge).

• Provider ease of placement (as rated by a visual analog scale or other tool).

• Need for adjunctive placement measures (e.g. cervical dilation, ultrasound guidance, local anesthesia, analgesia).

• Placement success.

• Patient satisfaction with procedure (as rated by a Likert a scale or other tool before clinic discharge).

• Local anesthetic side effects occurring before clinic discharge (each of the following will be reported: vomiting, dizziness, tinnitus, circumoral numbness, tongue paresthesia).

• Adverse events occurring before clinic discharge (each of the following will be reported: uterine perforation, vasovagal reaction, cervical laceration, seizure, respiratory failure, cardiac arrest).

We will use footnotes to give justifications for our decisions to downgrade the certainty of the evidence and provide comments to aid readers’ understanding of the review where necessary. Two review authors (LZ, AN) will make independent judgments about the certainty of the evidence, with disagreements resolved by discussion or involving a third author (KC). We will justify the judgments, document them, and incorporate them into reporting of results for each outcome.

Appendices

Appendix 1. Model search strategy

MEDLINE ALL Ovid <1946 to 5 July 2022>

Date searched: 6 July 2022

1 intrauterine devices/ or intrauterine devices, medicated/ or intrauterine devices, copper/ (12022)

2 (IUB or IUBs or IUC or IUCs or IUD or IUDs or IUCD or IUCDs or IUS or IUSs or CuIUB or Cu‐IUB or CuIUBs or Cu‐IUBs or CuIUD or Cu‐IUD or CuIUDs or Cu‐IUDs or CuIUC or Cu‐IUC or CuIUCs or Cu‐IUCs or CuIUCD or Cu‐IUCD or CuIUDs or Cu‐IUDs or CuIUS or Cu‐IUS or CuIUSs or Cu‐IUSs or ECIUD* or LNGIUC or LNGIUCs or LNGIUCD or LNGIUCDs or LNGIUD or LNGIUDs or LNGIUS or LNGIUSs).ti,ab,kf. (11542)

3 ((intrauterine or intra‐uterine) adj3 (ball or balls or coil or coils or contraceptive or contraception or device or devices or system or systems)).ti,ab,kf. (10180)

4 (Kyleena or Liletta or Mirena or Skyla or Copper‐7 or Copper‐T or CuSafe or Cu‐Safe or Cu375 or Cu‐375 or "Cu 7" or "Cu T" or "Cu T‐200" or CuT200 or CuT380* or Cu‐T380* or FlexiT or Flexi‐T or FlexiT300 or Flexi‐T300 or Gyne or Gynefix or Gyneplus or Gyne‐T380S or Liberte or "Lippes Loop" or Load‐375 or MCu or MLCu* or "ML Cu375" or Mini380 or "Mini 380" or Mini‐TT or MiniTT or "Mona Lisa" or Multiload or Multi‐load or MultiSafe or Multi‐Safe or MYCu or NeoSafe or Neo‐Safe or NovaT or Nova‐T or NovaT380 or Paragard or TCu or TSafe or T‐Safe or T380* or T‐380* or TT380* or TT‐380* or UT380 or UT‐380).ti,ab,kf. (4499)

5 or/1‐4 (21179)

6 Anesthesia, Local/ or exp Anesthetics, Local/ or Nerve Block/ or Benzocaine/ or Benzyl Alcohol/ or Bupivacaine/ or Carticaine/ or Cocaine/ or Dibucaine/ or Diphenhydramine/ or Ethyl Chloride/ or Etidocaine/ or Levobupivacaine/ or Lidocaine/ or Lidocaine, Prilocaine Drug Combination/ or Mepivacaine/ or Prilocaine/ or Procaine/ or Propoxycaine/ or Ropivacaine/ or Tetracaine/ or Tetrodotoxin/ or Trimecaine/ (132285)

7 (block or intracervical or intra‐cervical or paracervical or para‐cervical or ((cream or gel or local or spray or topical) adj4 (anesthe* or anaesthe*))).ti,ab,kf. (306157)

8 (benoxinate or Benzocaine or Benzyl Alcohol or bumecaine or Bupivacaine or butacaine or butamben or canertinib dihydrochloride or carbizocaine or Carticaine or chloroprocaine or Cocaine or Dibucaine or dimethisoquin or dimethocaine or Diphenhydramine or dyclonine or Ethyl Chloride or Etidocaine or heptacaine or Levobupivacaine or Lidocaine or Lignocaine or Mepivacaine or oxethazaine or pentacaine or pramoxine or Prilocaine or Procaine or propisomide or Propoxycaine or proxymetacaine or QX‐314 or Ropivacaine or TEC solution or Tetracaine or Tetrodotoxin or Trimecaine or tyrothricin).ti,ab,kf,nm,rn. (147722)

9 or/6‐8 (434546)

10 and/5,9 (319)

11 10 not ((Animals/ not Humans/) or (adhesions or animal or bovine or canine or capra or cat$1 or dog$1 or equine or feline or goat$1 or horse$1 or mice or mouse or ovine or rat$1 or rattus or rodent$3 or sheep).ti.) (289)

Contributions of authors

• Conceiving the protocol: Lauren Zapata (LZ), Antoinette Nguyen (AN), Emily Snyder (ES), Nathalie Kapp (NK), Angeline Ti (AT), Maura Whiteman (MW), and Kathryn Curtis (KC).

• Designing the protocol: LZ, AN, ES, NK, AT, MW, and KC.

• Co‐ordinating the protocol: LZ, AN, ES, NK, AT, MW, and KC.

• Designing search strategies: Robin Payter (Information Specialist).

• Writing the protocol: LZ, AN, ES, NK, AT, MW, and KC.

• Performing previous work that was the foundation of the current study: LZ, AN, ES, NK, AT, MW, and KC.

Sources of support

Internal sources

• No sources of support provided

External sources

• No sources of support provided

Declarations of interest

• Lauren Zapata: none known.

• Antoinette Nguyen: none known.

• Emily Snyder: none known.

• Angeline Ti: none known.

• Nathalie Kapp: none known

• Maura Whiteman: none known.

• Kathryn Curtis: none known.

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