Evaluating the translational value of preclinical models: Available tools and frameworks, challenges and strategies
Bibliographic record
Abstract
Recent global initiatives are accelerating the shift toward human-centric approaches, reducing reliance on animal models in preclinical research and other domains. In this changing landscape, objectively evaluating the scientific relevance and merit of research involving animal models, and assessing their translational relevance is increasingly critical. Over the past decade, several tools have been developed to assess translational relevance, accuracy/appropriateness and efficacy of preclinical animal models, evaluate risk-of-bias in preclinical research, support harm-benefit analyses, and facilitate the adoption of non-animal replacement strategies. However, the uptake of such tools remains limited. To address this, a Biomedical Research for the 21st Century (BioMed21) Collaboration workshop on 'Evaluating translational value of animal models in preclinical research - Tools, challenges, and strategies', was convened by Humane World for Animals (30 June-1 July 2025). The event brought together tool developers and diverse global interest-holders to review current assessment tools, discuss their strengths, complementarity, limitations and feasibility, and explore opportunities for cross-sector collaboration. This paper summarises key outcomes of these presentations and discussions, highlighting knowledge gaps and barriers to the adoption of these tools and frameworks by researchers, funders and regulators. Strategies to raise awareness and promote the use of the tools and frameworks, to better inform funding decisions, regulatory approval and the appraisal of preclinical research, are also proposed.
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 | 0.513 | 0.391 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.033 | 0.030 |
| Open science | 0.013 | 0.024 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".