Skin Temperature during Cutaneous Wound Healing in an Equine Model of Cutaneous Fibroproliferative Disorder: Kinetics and Anatomic‐Site Differences
Bibliographic record
Abstract
OBJECTIVE: To map skin temperature kinetics, and by extension skin blood flow throughout normal or abnormal repair of full-thickness cutaneous wounds created on the horse body and limb, using infrared thermography. STUDY DESIGN: Experimental. ANIMALS: Standardbreds (n = 6), aged 3-4 years. METHODS: Three cutaneous wounds were created on the dorsolateral surface of each metacarpus and on the lateral thoracic wall. Thoracic skin wounds and those on 1 randomly chosen forelimb healed by second intention without a bandage, whereas contralateral limb wounds were bandaged to induce formation of exuberant granulation tissue (EGT). Thermal data were collected from all planned wound sites before the surgical procedure (baseline), and at 24, 48, 96 hours, 1, 2, and 4 weeks after wounding. Data were analyzed using repeated measures ANOVA and a priori contrasts submitted to Bonferroni sequential correction. Level of significance was P < .05. RESULTS: Cutaneous wound temperature (CWT) increased temporally from preoperative period to week 1 postwounding, independently of anatomic location (P < .0001). CWT of limb wounds was significantly less than that of body wounds throughout healing (P < .01). CWT of limb wounds managed with bandages and developing EGT was significantly less than that of unbandaged limb wounds, which did not develop EGT (P ≤ .01). CONCLUSIONS: CWT varied with anatomic location and throughout healing. CWT of wounds developing EGT was significantly less than that of wounds without 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".