An introduction to methodological issues when including non‐randomised studies in systematic reviews on the effects of interventions
Bibliographic record
Abstract
BACKGROUND: Methods need to be further developed to include non-randomised studies (NRS) in systematic reviews of the effects of health care interventions. NRS are often required to answer questions about harms and interventions for which evidence from randomised controlled trials (RCTs) is not available. Methods used to review randomised controlled trials may be inappropriate or insufficient for NRS. AIM AND METHODS: A workshop was convened to discuss relevant methodological issues. Participants were invited from important stakeholder constituencies, including methods and review groups of the Cochrane and Campbell Collaborations, the Cochrane Editorial Unit and organisations that commission reviews and make health policy decisions. The aim was to discuss methods for reviewing evidence when including NRS and to formulate methodological guidance for review authors. WORKSHOP FORMAT: The workshop was structured around four sessions on topics considered in advance to be most critical: (i) study designs and bias; (ii) confounding and meta-analysis; (iii) selective reporting; and (iv) applicability. These sessions were scheduled between introductory and concluding sessions. SUMMARY: This is the first of six papers and provides an overview. Subsequent papers describe the discussions and conclusions from the four main sessions (papers 2 to 5) and summarise the proposed guidance into lists of issues for review authors to consider (paper 6). Copyright © 2013 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.602 | 0.744 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.021 | 0.025 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.014 | 0.016 |
| Research integrity | 0.025 | 0.048 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".