Regional differences in wound oxygenation during normal healing in an equine model of cutaneous fibroproliferative disorder
Bibliographic record
Abstract
Wound repair in horse limbs is often complicated by the development of exuberant granulation tissue (EGT) and excessive scarring while body wounds tend to repair uneventfully. EGT resembles the human keloid. While the events leading to keloid formation are not fully elucidated, tissue hypoxia has been proposed as a major contributing factor. The objective of this study was to investigate tissue oxygen saturation in healing full-thickness wounds created on the horse limb and body, using near-infrared spectroscopy. Spectroscopic reflectance data were collected from both anatomic sites at specific times following wounding. The oxygen saturation values of limb wounds were significantly inferior to those of body wounds during the early period of healing, indicating a temporary, relative state of hypoxia in the former during the inflammatory phase of repair. Horses present a weak, persistent inflammatory response to wounding, especially at the limb level. The relative hypoxia present acutely in limb wounds of horses may promote a feeble yet prolonged inflammatory response, which could interfere with and retard the subsequent phases of healing. Ongoing low-grade inflammation in horse wounds is accompanied by up-regulation of various inflammatory and profibrotic mediators, which might ultimately promote the development of fibroproliferative disorders such as EGT.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".