Checklists of methodological issues for review authors to consider when including non‐randomized studies in systematic reviews
Bibliographic record
Abstract
BACKGROUND: There is increasing interest from review authors about including non-randomized studies (NRS) in their systematic reviews of health care interventions. This series from the Ottawa Non-Randomized Studies Workshop consists of six papers identifying methodological issues when doing this. AIM: To format the guidance from the preceding papers on study design and bias, confounding and meta-analysis, selective reporting, and applicability/directness into checklists of issues for review authors to consider when including NRS in a systematic review. CHECKLISTS: Checklists were devised providing frameworks to describe/assess: (1) study designs based on study design features; (2) risk of residual confounding and when to consider meta-analysing data from NRS; (3) risk of selective reporting based on the Cochrane framework for detecting selective outcome reporting in trials but extended to selective reporting of analyses; and (4) directness of evidence contributed by a study to aid integration of NRS findings into summary of findings tables. SUMMARY: The checklists described will allow review groups to operationalize the inclusion of NRS in systematic reviews in a more consistent way. The next major step is extending the existing Cochrane Risk of Bias tool so that it can assess the risk of bias to NRS included in a review. 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 | 0.546 | 0.811 |
| Meta-epidemiology (narrow) | 0.009 | 0.009 |
| Meta-epidemiology (broad) | 0.011 | 0.023 |
| Bibliometrics | 0.035 | 0.030 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.011 | 0.014 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".