Hypertrophic versus non hypertrophic scars compared by immunohistochemistry and laser confocal microscopy: type I and III collagens<sup>†</sup>
Bibliographic record
Abstract
Although dermal collagens appear increased in hypertrophic scars, this has not been tested in tissue samples using objective methods. We compared the expression of types I and III collagen in hypertrophic and non hypertrophic scars at 6-12 and 18-24 months after burn using a quantitative method. Among 17 patients with extensive burns, 3 patients had acute scars, 8 had hypertrophic or non hypertrophic scars at 6-12 months after burn and 6 had hypertrophic or non hypertrophic scars at 18-24 months after burn. After clinical assessment of scars using the Vancouver scale, immunohistochemistry for types I and III collagens was performed. Images were captured with a laser scanning confocal microscope and the relative amounts of types I and III collagens were determined in superficial and deep dermis. The effects of time and scar type were assessed using two-way analysis of variance (ANOVA) and Tukey's test. Collagen III scar/normal ratios were higher in hypertrophic scars at both time points (P = 0.05). There were no differences in collagen I scar/normal ratios. Large variation was observed in scars during the acute phase regarding the expression of collagens. Easily accessed by immunohistochemistry and confocal microscopy, type III collagen deposition may help in determining scar phenotype, differentiating hypertrophic and non hypertrophic scars.
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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 | 0.000 |
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".