Pathology Study Design, Conduct, and Reporting to Achieve Rigor and Reproducibility in Translational Research Using Animal Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In translational research, animal models are an important tool to aid in decision-making when taking potential therapies into human clinical trials. Recently, there have been a number of papers that have suggested limited concordance of preclinical animal experiments with subsequent human clinical experience. Assessments of preclinical animal studies have led to concerns about the reproducibility of data and have highlighted the need for an emphasis on rigor and quality in the planning, conduct, analysis, and reporting of such studies. The incorporation of a wider role for the comparative pathologist using pathology best practices in the planning and conduct of animal model-based research is one way to increase the quality and reproducibility of data. The use of optimal design and planning of tissue collection, incorporation of pathology methods into written protocols, conduct of pathology procedures using accepted best practices, and the use of optimal pathology analysis and reporting methods enhance the quality of the data acquired from many types of preclinical animal models and studies. Many of these pathology practices are well established in the discipline of toxicologic pathology and have a proven and useful track record in enhancing the data from animal-based studies used in safety assessment of human therapeutics. Some of this experience can be adopted by the wider community of preclinical investigators to increase the reproducibility of animal study data.
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.
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 | 0.018 | 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 | 0.001 |
| 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 it