Issues relating to selective reporting when including non‐randomized studies in systematic reviews on the effects of healthcare interventions
Bibliographic record
Abstract
BACKGROUND: Selective outcome and analysis reporting (SOR and SAR) occur when only a subset of outcomes measured and analyzed in a study is fully reported, and are an important source of potential bias. KEY METHODOLOGICAL ISSUES: We describe what is known about the prevalence and effects of SOR and SAR in both randomized controlled trials (RCTs) and non-randomized studies (NRS), and the effects of SOR and SAR on summary effect estimates and conclusions in systematic reviews of the effectiveness of healthcare interventions. GUIDANCE: Review authors should always suspect SOR and SAR in reviews that include NRS, assess primary studies for the risk of bias, and make reasonable attempts to retrieve study protocols or other documentation developed before study recruitment began. There are clues that may suggest SOR or SAR in NRS, including differences between the methods and results sections of the publication, study funder, and differences between study protocol or registration information and the study report. CONCLUSION: Existing evidence about reporting biases in primary studies comes almost exclusively from methodological reviews of RCTs. The prevalence and impact of SOR and SAR in NRS are likely even greater than in RCTs but it is difficult to identify and confirm selective reporting in NRS. Copyright © 2012 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 | 0.804 | 0.921 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.025 | 0.037 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.012 | 0.010 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".