INSPECT-SR: a tool for assessing trustworthiness of randomised controlled trials
Bibliographic record
Abstract
The integrity of evidence synthesis is threatened by problematic randomised controlled trials (RCTs). These are RCTs where there are serious concerns about the trustworthiness of the data or findings. This could be due to research misconduct, including fraud, or due to honest critical errors. If these RCTs are not detected, they may be inadvertently included in systematic reviews and guidelines, potentially distorting their results. To address this problem, the INSPECT-SR (INveStigating ProblEmatic Clinical Trials in Systematic Reviews) tool has been developed to assess the trustworthiness of RCTs. This will allow problematic RCTs to be identified and excluded from systematic reviews. This paper describes the development of INSPECT-SR. The tool and an associated guidance document are presented.
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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 | 0.529 | 0.877 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.043 | 0.031 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.086 | 0.019 |
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".