Preclinical efficacy studies in investigator brochures: Do they enable risk–benefit assessment?
Bibliographic record
Abstract
Human protection policies require favorable risk-benefit judgments prior to launch of clinical trials. For phase I and II trials, evidence for such judgment often stems from preclinical efficacy studies (PCESs). We undertook a systematic investigation of application materials (investigator brochures [IBs]) presented for ethics review for phase I and II trials to assess the content and properties of PCESs contained in them. Using a sample of 109 IBs most recently approved at 3 institutional review boards based at German Medical Faculties between the years 2010-2016, we identified 708 unique PCESs. We then rated all identified PCESs for their reporting on study elements that help to address validity threats, whether they referenced published reports, and the direction of their results. Altogether, the 109 IBs reported on 708 PCESs. Less than 5% of all PCESs described elements essential for reducing validity threats such as randomization, sample size calculation, and blinded outcome assessment. For most PCESs (89%), no reference to a published report was provided. Only 6% of all PCESs reported an outcome demonstrating no effect. For the majority of IBs (82%), all PCESs were described as reporting positive findings. Our results show that most IBs for phase I/II studies did not allow evaluators to systematically appraise the strength of the supporting preclinical findings. The very rare reporting of PCESs that demonstrated no effect raises concerns about potential design or reporting biases. Poor PCES design and reporting thwart risk-benefit evaluation during ethical review of phase I/II studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.829 | 0.934 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.017 | 0.035 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".