Effects of platelet-rich plasma on the repair of wounds on the distal aspect of the forelimb in horses
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effect of platelet-rich plasma on wounds on the distal aspect of the forelimb in horses. ANIMALS: 6 mixed-breed 10- to 15-year-old mares. PROCEDURES: 3 wounds were created on metacarpal regions in each of 6 horses (n = 36 wounds total). Eighteen wounds were treated with platelet-rich plasma and bandaged, whereas 18 control wounds were similarly bandaged with no prior topical treatment. Decrease in wound surface area and the required number of excisions of exuberant granulation tissue were recorded until complete healing. Tissue specimens were taken from wounds at 1 week for histologic examination and measurement of transforming growth factor-beta1 concentrations and at closure for histologic examination, biomechanical evaluation, and measurement of collagen type I and type III mRNA. RESULTS: Platelet-rich plasma favored excessive development of granulation tissue and significantly slowed wound healing at 1, 2, and 3 weeks after surgery. Transforming growth factor-beta1 had a 1.6-fold higher concentration in treated wounds, compared with untreated wounds. Histologic, biomechanical, and gene expression data did not differ significantly between treated and control wounds. CONCLUSIONS AND CLINICAL RELEVANCE: Topical application of autologous platelet-rich plasma did not accelerate or improve the quality of repair of small granulating wounds on limbs of horses. This treatment may better suit wounds with massive tissue loss or, alternatively, chronic wounds that would benefit from a fresh source of mediators to accelerate the healing process.
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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".