Clinical relevance assessment of animal preclinical research (RAA) tool: development and explanation
Bibliographic record
Abstract
BACKGROUND: Only a small proportion of preclinical research (research performed in animal models prior to clinical trials in humans) translates into clinical benefit in humans. Possible reasons for the lack of translation of the results observed in preclinical research into human clinical benefit include the design, conduct, and reporting of preclinical studies. There is currently no formal domain-based assessment of the clinical relevance of preclinical research. To address this issue, we have developed a tool for the assessment of the clinical relevance of preclinical studies, with the intention of assessing the likelihood that therapeutic preclinical findings can be translated into improvement in the management of human diseases. METHODS: We searched the EQUATOR network for guidelines that describe the design, conduct, and reporting of preclinical research. We searched the references of these guidelines to identify further relevant publications and developed a set of domains and signalling questions. We then conducted a modified Delphi-consensus to refine and develop the tool. The Delphi panel members included specialists in evidence-based (preclinical) medicine specialists, methodologists, preclinical animal researchers, a veterinarian, and clinical researchers. A total of 20 Delphi-panel members completed the first round and 17 members from five countries completed all three rounds. RESULTS: This tool has eight domains (construct validity, external validity, risk of bias, experimental design and data analysis plan, reproducibility and replicability of methods and results in the same model, research integrity, and research transparency) and a total of 28 signalling questions and provides a framework for researchers, journal editors, grant funders, and regulatory authorities to assess the potential clinical relevance of preclinical animal research. CONCLUSION: We have developed a tool to assess the clinical relevance of preclinical studies. This tool is currently being piloted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; a candidate call from one teacher head, not a consensus.
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".