EQUATOR-Oncology: reducing the latitude of cancer trial design and reporting
Bibliographic record
Abstract
Well-designed and appropriately reported clinical trials are essential to evaluate treatment efficacy and form the basis for regulatory approval of new cancer treatments, post-marketing funding decisions, and endorsement by the wider oncology community. In contrast, poorly designed or inadequately reported studies can impair the clinical relevance of these results. Inaccurate or unreproducible data can ill-advise on further study of new treatments, or may result in the inability to translate benefit observed in clinical trials into an improvement in patient outcome in routine practice. Problems which may influence the interpretation of trials include: the use of narrow eligibility criteria that limits generalisability, the use of surrogate end points that have not been validated as reflecting patient benefit, the reporting of statistically significant but clinically less meaningful results, the underestimation of toxicity, and biased reporting – both in the primary publication and by the media ( Tannock et al, 2016 ). To ensure the highest fidelity in clinical research, it is important to have a framework for optimal design and reporting of clinical trials. Although a number of reporting criteria ( Schulz et al, 2010 ; von Elm et al, 2007 ) have been developed and endorsed by journal editors and the research community, few have focused exclusively on oncology trials.
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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 | 0.356 | 0.703 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.037 | 0.038 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".