Equine exuberant granulation tissue and human keloids: A comparative histopathologic study
Bibliographic record
Abstract
OBJECTIVE: To compare histopathologic features of a fibroproliferative disorder in horses (exuberant granulation tissue-EGT) and people (keloid). SAMPLE POPULATION: Archival tissue samples of EGT (n = 8) and keloid (12). METHODS: After automated hematoxylin and eosin, histochemical (Gomori trichrome, Verhoeff-van Gieson elastin) and immunohistochemical (vimentin, α-smooth muscle actin, CD34, CD68, CD117) stainings, tissue sections were evaluated using a semi-quantitative grading scale for presence or absence of ulceration, keloidal collagen, myofibroblasts, and elastic fibers as well as degree of inflammation, fibrosis, vascularity, and orientation of collagen fibers. RESULTS: Superficial dermis and deep dermis of both horses and people had increased numbers of haphazardly oriented thickened collagen fibers; however, only keloids contained "keloidal" collagen. Fibroblast numbers were markedly increased in both groups but only EGT had myofibroblasts. Minimal vascularity was observed in the deep dermis of both groups. The superficial dermis in EGT was characterized by small vessels within immature granulation tissue. Macrophages and mast cells were infrequently found in both groups but polymorphonuclear cells were markedly increased in EGT. CONCLUSIONS: Humans and horses are the only mammals known to naturally develop excessive granulation during wound healing; however, similarities and differences between fibroblast populations and associated collagen have not been reported. Inflammatory response may contribute to observed differences in the cellular populations, with EGT possessing markedly increased myofibroblasts, small vessels, and acute inflammatory cells compared with keloids. Further work is warranted to develop common treatment strategies for these fibroproliferative conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".