Issues relating to selective reporting when including non‐randomized studies in systematic reviews on the effects of healthcare interventions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.889 | 0.980 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it