Table 4.

Understanding of SoF tables

Examples for tables	Explanations
Outcomes	The tables provide the findings for the main outcomes for someone making a decision, which were prioritized by the panel. These include potential benefits and harms, which are listed whether the included studies provide data for these outcomes or not.
No. of participants (studies): 1519 (11 observational studies) (recurrent major VTE outcome)	The table provides the total number of participants across studies (1519 in this example) and the number of studies11 that provided data for that outcome. This indicates how much evidence there is for the outcome. The references for the included studies are listed below the table.
Certainty in the evidence (GRADE)	The quality of the evidence is a judgment about the extent to which we can be confident that the estimates of effect are correct. These judgments are made following the GRADE approach and are provided for each outcome. The judgments are based on the type of study design (randomized trials vs observational studies), the risk of bias, the consistency of the results across studies, the directness of the evidence, and the precision of the overall estimate across studies. For each outcome, the quality of the evidence is rated as high, moderate, low, or very low.
Relative effect (95% CI): RR 0.39 (0.21-0.72) (recurrent major VTE outcome)	Relative effects are ratios. Here, the relative effect is expressed as an RR. Risk is the probability of an outcome occurring. An RR is the ratio between the risk in the intervention group and the risk in the control group. If the risk in the intervention group is 1% (10 per 1000) and the risk in the control group is 10% (100 per 1000), the relative effect is 10/100 or 0.10. If the RR is exactly 1.0, this means that there is no difference between the occurrence of the outcome in the intervention and the control group. If the RR is >1.0, the intervention increases the risk of the outcome. If it is a good outcome (for example, the birth of a healthy baby), an RR >1.0 indicates a desirable effect for the intervention; whereas, if the outcome is bad (for example, VTE), an RR >1.0 would indicate an undesirable effect. In the example in Table 3, the RR of 0.39 informs us that the risk of VTE was decreased with the intervention, which represents a desirable effect.
Confidence interval	A CI is a range around an estimate that conveys how precise the estimate is; in this example, the result is the estimate of the intervention risk. The CI is a guide to how sure we can be about the quantity we are interested in (here the anticipated absolute effect). The narrower the range between the 2 numbers, the more confident we can be about what the true value is; the wider the range, the less sure we can be. The width of the CI reflects the extent to which we are uncertain in the observed estimate (with a wider interval reflecting more uncertainty).
95% CI	As explained previously, the CI indicates the extent to which chance may be responsible for the observed numbers. In the simplest terms, a 95% CI means that, if we repeat the same study infinite times, the true size of effect will be included between the lower and upper confidence limit (eg, 0.21 and 0.72 in the example of a relative effect recurrent major VTE in Table 3) in 95% of those studies. Conversely, 5% of the studies will provide 95% CIs that do not include the true size of effect.
Anticipated absolute effects	Absolute risks Risk is the probability of an outcome occurring. The estimated risks columns in the SoF table present the best estimate of the risk in the control group (risk with no antepartum anticoagulant prophylaxis in the Table 3 example) and the reduction risk in the intervention group (risk with antepartum anticoagulant prophylaxis in the Table 3 example), expressed as a value per 1000 patients, with a CI around the risk in the intervention group. Some versions of SoF tables include the risk in the intervention group without the risk difference. This can be chosen by the creator.
Estimated risk control: 42 per 1000 (recurrent major VTE outcome)	Estimated control risks (without the intervention; risk with no antepartum anticoagulant prophylaxis in the example in Table 3) are typical rates of an outcome occurring without the intervention. They will ideally be based on observational studies of incidence in representative populations. Alternatively, if such studies are not available, they can be based on the control group risks in comparative studies. When only 1 control group risk is provided, it is normally the median control group risk across the studies that provided data for that outcome. In this example (recurrent major VTE), the risk of 42 events occurring in every 1000 people indicates what would happen in a typical control group population. When relevant, the tables will provide information for >1 population, for instance differentiating between people at low and high risk when there are potentially important differences.
Intervention risk: 26 fewer per 1000 (33 fewer to 12 fewer) (recurrent major VTE outcome)	In this example, the estimated risk in the control group was 42 events in every 1000 persons. Implementing the intervention in this population would result in an intervention group risk of 16 events in every 1000 people, given the pooled RR across studies. The intervention results in 26 fewer patients with VTE events in every 1000 with a corresponding CI. If the table provides >1 control risk for an outcome, for instance, differentiating between people at low and high risk, then an intervention risk is provided for each population. Determining the effect of the intervention requires subtraction.
Difference between relative and absolute effects	The effect of an intervention can be described by comparing the risk of the control group with the risk of the intervention group. Such a comparison can be made in different ways. One way to compare 2 risks is to calculate the difference between the risks. This is the absolute effect. The absolute effect can be found in the SoF table by calculating the difference between the numbers in the control risk in the control group on the left and the intervention risk in the intervention group on the right. Here is an example: consider the risk for blindness in a patient with diabetes over a 5-y period. If the risk for blindness is found to be 20 in 1000 (2%) in a group of patients treated conventionally and 10 in 1000 (1%) for patients treated with a new drug, the absolute effect is derived by subtracting the intervention group risk from the control group risk: 2% − 1% = 1%. Expressed in this way, it can be said that the new drug reduces the 5-y risk for blindness by 1% (absolute effect is 10 fewer per 1000). Another way to compare risks is to calculate the ratio of the 2 risks. Given the data in the blindness example, the relative effect is derived by dividing 2 risks, with the intervention risk being divided by the control risk: 1%/2% = 1/2 (0.50). Expressed in this way, as the ‘‘relative effect,’’ the 5-y risk for blindness with the new drug is one-half the risk with the conventional drug. Here, the table presents risks as times per 1000 instead of as a percentage, as this tends to be easier to understand. Whenever possible, the table presents the relative effect as the RR. Usually the absolute effect is different for groups that are at high and low risk, whereas the relative effect often is the same. Therefore, when it is relevant, GRADE tables report risks for groups at different levels of risk.
Explanations	Explanatory notes are provided below the table and include explanations of the judgments for rating down the certainty in the evidence, as well as any additional clarifications for users.

Schünemann.⁴⁰