The absolute measure of intervention effects is the difference between the baseline risk of an outcome (for example, in patients receiving control interventions or estimated in the observational studies) and the risk of outcome after the intervention is applied; that is, the risk of an outcome in people who were exposed to or received an intervention. Absolute effect is based on the relative magnitude of an effect and baseline risk.

A systematic error or deviation in results or inferences from the truth. In studies of the effects of health care, the main types of bias arise from systematic differences in the groups that are compared (selection bias), the care that is provided, exposure to other factors apart from the intervention of interest (performance bias), withdrawals or exclusions of people entered into a study (attrition bias) or how outcomes are assessed (detection bias). Systematic reviews of studies may also be particularly affected by reporting bias, where a biased subset of all the relevant data is available.

An outcome that has been assessed as 7–9 on a scale of 1–9 for the importance of the outcome when making decisions about the optimal management strategy.

The relationship between the quantity of treatment given and its effect on outcome. This factor may increase confidence in the results.

A table summarizing the quality of the available evidence, the judgements that bear on the quality rating and the effects of alternative management strategies on the outcomes of interest. It includes an explicit judgement of each factor determining the quality of evidence for each outcome. It should be used by guideline panels to ensure that they agree about the judgements underlying the quality assessments and to establish the judgements.

We are very confident that the true effect lies close to that of the estimate of the effect.

An outcome that has been assessed as 4–6 on a scale of 1–9 for the importance of the outcome when making decisions about the optimal management strategy. It is important but not critical.

Refers to whether the results are precise enough. When assessing imprecision, guideline panels need to consider the context of a recommendation and other outcomes, whereas authors of systematic reviews need only to consider the imprecision for a specific outcome. Authors should consider width of confidence intervals, number of patients (optimal information size) and number of events.

Refers to widely differing estimates of the treatment effect (that is, heterogeneity or variability in results) across studies that suggest true differences in underlying treatment effect. When the magnitude of intervention effects differs, explanations may lie in the patients (e.g. disease severity), the interventions (e.g. doses, co-interventions, comparison interventions), the outcomes (e.g. duration of follow-up) or the study methods (e.g. randomized trials with higher and lower quality risk of bias).

Refers to whether the evidence directly answers the health-care question. Indirectness may occur when we have no direct or head-to-head comparisons between two or more interventions of interest; it may occur also when the question being addressed by the guideline panel or by the authors of a systematic review is different from the available evidence regarding the population, intervention, comparator or an outcome.

Our confidence in the effect estimate is limited: The true effect may be substantially different from the estimate of the effect

We are moderately confident in the effect estimate: The true effect is likely to be close to the estimate of the effect, but there is a possibility that it is substantially different.

Refers to a body of evidence not to individual studies (that is, means more than risk of bias of studies). It includes consideration of risk of bias, imprecision, inconsistency, indirectness and publication bias, as well as the magnitude of treatment effect and the presence of a dose– response gradient. In the context of a systematic review, the ratings of the quality of evidence reflect the extent of our confidence that the estimates of the effect are correct. In the context of making recommendations, the quality ratings reflect the extent of our confidence that the estimates of an effect are adequate to support a particular decision or recommendation.

An experimental study in which two or more interventions are compared by being randomly allocated to participants. In most trials, one intervention is assigned to each individual but sometimes assignment is to defined groups of individuals (for example, in a household) or interventions are assigned within individuals (for example, in different orders or to different parts of the body).

The relative effect for a dichotomous outcome from a single study or a meta-analysis will typically be a risk ratio (relative risk), odds ratio or, occasionally, a hazard ratio.

The degree of confidence that the desirable effects of adherence to a recommendation outweigh the undesirable effects. Either strong or weak/conditional.

Most patients would want the recommended course of action, and only a small proportion would not; therefore, clinicians should provide the intervention. The recommendation can be adapted as policy in most situations.

The risk of misleading results is a result of flawed design or conduct of randomized or observational studies. It is one of the five categories of reasons for downgrading the quality of evidence. It includes lack of allocation concealment; lack of blinding; incomplete accounting of patients and outcomes events; selective outcome reporting bias; and other limitations, such as stopping early for benefit, use of non- validated outcome measures, carryover effects in crossover trials, and recruitment bias in cluster-randomized trials.

Outcome measure that is not of direct practical importance but is believed to reflect an outcome that is important; for example, blood pressure is not directly important to patients but it is often used as an outcome in clinical trials because it is a risk factor for stroke and heart attacks. Surrogate outcomes are often physiological or biochemical markers that can be relatively quickly and easily measured, and that are taken as being predictive of important clinical outcomes. They are often used when observation of clinical outcomes requires long follow-up. Also called: intermediary outcomes or surrogate end-points.

We have very little confidence in the effect estimate: the true effect is likely to be substantially different from the estimate of effect.

The majority of patients would want the suggested course of action, but many would not. Clinicians should recognize that different choices will be appropriate for individual patients, and that they must help each patient arrive at a management decision consistent with his or her values and preferences. Policy-making will require substantial debate and involvement of various stakeholders.