A comprehensive welfare scoring system for graft versus host disease clinical assessment in humanised mouse models used for pharmaceutical research
Bibliographic record
Abstract
Immuno-oncology drug discovery increasingly relies on humanised mouse models of cancer due to limitations of murine surrogate tools and differences between mouse and human immune systems. Graft-versus-Host Disease (GvHD) is a significant complication following xenogeneic transplantation of human immune cells into mice, limiting their lifespan and impacting the utility of these studies. Existing GvHD scoring systems inadequately capture the disease's complexity, hampering optimal welfare management and clinical progression monitoring. We propose a comprehensive, practical scoring system for monitoring clinical signs of GvHD in humanised mice. It evaluates seven clinical signs reflecting disease complexity, sums the scores, and categorises overall GvHD severity into four stages, each with specific welfare actions. This refined tool reduces animal suffering through early detection and timely interventions, enabling mice to remain on studies where possible to maximise scientific impact. Our scoring system correlates with histological scores of GvHD-induced tissue damage across multiple organs, with liver and kidney histopathology ranking highly, unlike lung pathology. The system is reproducible among independent experimenters and versatile, effectively applied across multiple types of humanised mouse models and strains. It identifies common clinical signs including weight loss, swelling/reddening of extremities, fur condition, and posture changes, aiding users in distinguishing relevant signs. This system refines and standardises welfare decision-making, supporting the responsibility to minimise suffering when working with humanised mice.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".