How well are Phase 2 cancer trial publications supported by preclinical efficacy evidence?
Bibliographic record
Abstract
Major ethics policies require that human studies be preceded by animal experiments. We probed the extent to which trials testing efficacy of cancer drugs cited preclinical efficacy studies testing the same drug and disease indication. Using a sample of Phase 2 trial publications for novel cancer monotherapies approved by Food and Drug Administration 2005-2007, we conducted a systematic analysis of citations to preclinical efficacy evidence within trial publications. Citations were classified based on whether they "matched" the drug and indication of the trial. Our sample included 179 Phase 2 publications published 2004-2016. At least one preclinical study was cited for 113 of 179 publications (63%); 56 (31%) cited matching preclinical studies, and 74 (41%) did not cite either matching preclinical or matching clinical trial evidence. When excluding evidence that would likely not have been available to investigators before trial launch, 45 trials (25%) cited matching preclinical studies; 91 (51%) did not cite any matched preclinical or clinical, preceding evidence. No relationship between citation of matching and preceding preclinical evidence and trial outcomes was observed (28.4% of nonpositive trials vs. 26.9% of positive trials, p ~ 1). This suggests that many Phase 2 trial publications do not cite matching preclinical efficacy studies. Limited citation either suggests its absence or its exclusion from a publication. To ensure trials rest on a sound ethical basis and that publications support valid inference, journal editors and referees might encourage more complete descriptions of preclinical evidence or, where appropriate, active disclosure of its absence.
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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 | 0.427 | 0.854 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.037 | 0.045 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".