The Horse as a Model for the Study of Cutaneous Wound Healing
Bibliographic record
Abstract
Significance: Cutaneous wounds are a major problem in both human and equine medicine. The economic cost of treating skin wounds and related complications in humans and horses is high, and in both species, particular types of chronic wounds do not respond well to current therapies, leading to suffering and morbidity. Recent Advances: Conventional methods for the treatment of cutaneous wounds are generic and have not changed significantly in decades. However, as more is learned about the mechanisms involved in normal skin wound healing, and how failure of these processes leads to chronic nonhealing wounds, novel therapies targeting the specific pathologies of hard-to-heal wounds are being developed and evaluated. Critical Issues: Physiologically relevant animal models are needed to (1) study the mechanisms involved in normal and impaired skin wound healing and (2) test newly developed therapies. Future Directions: Similarities in normal wound healing in humans and horses, and the natural development of distinct types of hard-to-heal chronic wounds in both species, make the horse a physiologically relevant model for the study of mechanisms involved in wound repair. Horses are also well-suited models to test novel therapies. In addition, studies in horses have the potential to benefit veterinary, as well as human medicine.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".