The effects of topical collagen treatment on wound breaking strength and scar cosmesis in rats
Bibliographic record
Abstract
BACKGROUND: Topical application of collagen has been suggested to enhance wound healing; however, its long-term effect on wounds has not been studied in a rat model. HYPOTHESIS: Topical application of collagen type I will not facilitate incision healing or cosmesis in rats up to 28 days postwounding. METHODS: The effects of bovine collagen type I (6 mg/mL) on the rat surgical paired skin incision model were examined. Each rat served as its own control in which topical collagen was applied to one incision while normal saline (0.9%) was applied to the other incision. Rats were euthanized three (n=6), seven (n=6) and 28 (n=5) days after wounding. Tissue harvested from each time point was examined for maximal breaking strength, and for biochemical and histological analysis. RESULTS: There were no statistically significant differences (ie, P<0.05) in maximum wound breaking strength between the collagen- and saline-treated wounds at all time points. Histological analysis revealed a similar infiltration of inflammatory cells and fibroblasts in the wound edges of all incisions when matched with time of wounding. Western blot analysis revealed no differences in fibronectin or collagen I content in all wounds in each rat. CONCLUSIONS: The topical application of collagen did not facilitate wound healing from three to 28 days in the rat wound model.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".