Blinded interpretation of study results can feasibly and effectively diminish interpretation bias
Bibliographic record
Abstract
OBJECTIVE: Controversial and misleading interpretation of data from randomized trials is common. How to avoid misleading interpretation has received little attention. Herein, we describe two applications of an approach that involves blinded interpretation of the results by study investigators. STUDY DESIGN AND SETTINGS: The approach involves developing two interpretations of the results on the basis of a blinded review of the primary outcome data (experimental treatment A compared with control treatment B). One interpretation assumes that A is the experimental intervention and another assumes that A is the control. After agreeing that there will be no further changes, the investigators record their decisions and sign the resulting document. The randomization code is then broken, the correct interpretation chosen, and the manuscript finalized. Review of the document by an external authority before finalization can provide another safeguard against interpretation bias. RESULTS: We found the blinded preparation of a summary of data interpretation described in this article practical, efficient, and useful. CONCLUSIONS: Blinded data interpretation may decrease the frequency of misleading data interpretation. Widespread adoption of blinded data interpretation would be greatly facilitated were it added to the minimum set of recommendations outlining proper conduct of randomized controlled trials (eg, the Consolidated Standards of Reporting Trials statement).
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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 | 0.568 | 0.855 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| 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".