Outcome reporting bias in Cochrane systematic reviews: a cross-sectional analysis
Bibliographic record
Abstract
BACKGROUND: Discrepancies in outcome reporting (DOR) between protocol and published studies include inclusions of new outcomes, omission of prespecified outcomes, upgrade and downgrade of secondary and primary outcomes, and changes in definitions of prespecified outcomes. DOR can result in outcome reporting bias (ORB) when changes in outcomes occur after knowledge of results. This has potential to overestimate treatment effects and underestimate harms. This can also occur at the level of systematic reviews when changes in outcomes occur after knowledge of results of included studies. The prevalence of DOR and ORB in systematic reviews is unknown in systematic reviews published post-2007. OBJECTIVE: To estimate the prevalence of DOR and risk of ORB in all Cochrane reviews between the years 2007 and 2014. METHODS: A stratified random sampling approach was applied to collect a representative sample of Cochrane systematic reviews from each Cochrane review group. DOR was assessed by matching outcomes in each systematic review with their respective protocol. When DOR occurred, reviews were further assessed if there was a risk of ORB (unclear, low or high risk). We classified DOR as a high risk for ORB if the discrepancy occurred after knowledge of results in the systematic review. RESULTS: 150 of 350 (43%) review and protocol pairings contained DOR. When reviews were further scrutinised, 23% (35 of 150) of reviews with DOR contained a high risk of ORB, with changes being made after knowledge of results from individual trials. CONCLUSIONS: In our study, we identified just under a half of Cochrane reviews with at least one DOR. Of these, a fifth were at high risk of ORB. The presence of DOR and ORB in Cochrane reviews is of great concern; however, a solution is relatively simple. Authors are encouraged to be transparent where outcomes change and to describe the legitimacy of changing outcomes in order to prevent suspicion of bias.
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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 | 0.471 | 0.739 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.030 |
| Bibliometrics | 0.034 | 0.037 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".