Effect of a silicone-containing dressing on exuberant granulation tissue formation and wound repair in horses
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of a silicone dressing on the rate and quality of repair of limb wounds and compare microvascular occlusion and apoptosis in wounds treated with the silicone dressing and those treated with a conventional dressing in horses. ANIMALS: 5 horses. PROCEDURE: Horses received two 6.25-cm2 wounds on each metacarpus. Ten wounds were treated with a silicone dressing; the other 10 were treated with a control dressing. Quality of repair and wound size were evaluated at each bandage change. Time to healing and the number of excisions of exuberant granulation tissue were recorded. Biopsy specimens taken from healed wounds were evaluated semiquantitatively via histologic examination, p53 immunohistochemical analysis, terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling (TUNEL) to quantify apoptosis, and electron microscopic examination to measure microvessel luminal diameters. RESULTS: The silicone dressing surpassed the conventional dressing in preventing formation of exuberant granulation tissue and improving tissue quality. Microvessels were occluded significantly more often in wounds dressed with the silicone gel, which also diminished the expression of mutant p53, an indirect inhibitor of apoptosis, although greater apoptosis was not confirmed quantitatively by use of TUNEL. CONCLUSIONS AND CLINICAL RELEVANCE: Because the silicone dressing inhibited the formation of exuberant granulation tissue, it may be integrated in a management strategy designed to improve the repair of limb wounds in horses.
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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) | 0.000 | 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".