Aberrant wound healing in the horse: Naturally occurring conditions reminiscent of those observed in man
Bibliographic record
Abstract
Impaired wound healing represents an enormous clinical and financial problem for companion animals and humans alike. Unfortunately, most models used to study healing rely on rodents, which have significant differences in the healing and scarring process and rarely develop complications. In order to better simulate impaired healing, the model should strive to reproduce the natural processes of healing and delayed healing. Wounds on the limbs of horses display similarities to wounds in humans in their epithelialization/contraction ratio, genetic influence as well as dysregulated cytokine profile and the spontaneous development of fibroproliferative disorders. Veterinarians have access to advanced wound therapies that are often identical to those provided to human patients. Wound research in large animals has resulted in new wound models as well as a better understanding of the physiology, immunology, and local environmental impact on both normal and aberrant wound healing. One such model reproduces the naturally occurring fibroproliferative disorder of horses known as exuberant granulation tissue. Comparisons between the normally healing and impaired wounds provide insight into the repair process and can facilitate product development. A better understanding of the wound healing physiopathology based on clinically accurate animal models should lead to the development of novel therapies thereby improving outcomes in both human and veterinary patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".