Loss of data can occur when participants are compulsorily withdrawn from a group by the researchers (for example, when a per- protocol approach is used) or when participants do not attend assessment sessions. If the missing data are likely to be different from the data of those remaining in the groups, and there is a differential rate of such missing data from groups, systematic attrition bias may result. Selective outcome reporting Reporting of some outcomes and not others on the basis of the results can also lead to bias, as this may distort the overall impression of efficacy. Other limitations For example: Stopping early for benefit observed in randomised trials, in particular in the absence of adequate stopping rules. Use of unvalidated patient-reported outcome measures. Lack of washout periods to avoid carry-over effects in crossover trials. Recruitment bias in cluster-randomised trials. Indirectness refers to the extent to which the populations, interventions, comparisons and outcome measures are dissimilar to those defined in the inclusion criteria for the reviews. Indirectness is important when these differences are expected to contribute to a difference in effect size, or may affect the balance of harms and benefits considered for an intervention. As for the risk of bias, each outcome had its indirectness assessed within each study first. For each study, if there were no sources of indirectness, indirectness was given a rating of 0. If there was indirectness in just 1 source (for example in terms of population), indirectness was given a serious rating of 1, but if there was indirectness in 2 or more sources (for example, in terms of population and treatment) the indirectness was given a very serious rating of 2. A weighted average score was then calculated across all studies contributing to the outcome by taking into account study precision. 