Using the mouse to model human disease: increasing validity and reproducibility
Bibliographic record
Abstract
Experiments that use the mouse as a model for disease have recently come under scrutiny because of the repeated failure of data, particularly derived from preclinical studies, to be replicated or translated to humans. The usefulness of mouse models has been questioned because of irreproducibility and poor recapitulation of human conditions. Newer studies, however, point to bias in reporting results and improper data analysis as key factors that limit reproducibility and validity of preclinical mouse research. Inaccurate and incomplete descriptions of experimental conditions also contribute. Here, we provide guidance on best practice in mouse experimentation, focusing on appropriate selection and validation of the model, sources of variation and their influence on phenotypic outcomes, minimum requirements for control sets, and the importance of rigorous statistics. Our goal is to raise the standards in mouse disease modeling to enhance reproducibility, reliability and clinical translation of findings.
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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 | 0.040 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.011 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".