Basic fibroblast growth factor accelerates and improves second‐degree burn wound healing
Bibliographic record
Abstract
Second-degree burns are sometimes a concern for shortening patient suffering time as well as the therapeutic choice. Thus, adult second-degree burn patients (average 57.8 +/- 13.9 years old), mainly with deep dermal burns, were included. Patients receiving topical basic fibroblast growth factor (bFGF) or no bFGF were compared for clinical scar extent, passive scar hardness and elasticity using a Cutometer, direct scar hardness using a durometer, and moisture analysis of the stratum corneum at 1 year after complete wound healing. There was significantly faster wound healing with bFGF, as early as 2.2 +/- 0.9 days from the burn injury, compared with non-bFGF use (12.0 +/- 2.2 vs. 15.0 +/- 2.7 days, p<0.01). Clinical evaluation of Vancouver scale scores showed significant differences between bFGF-treated and non-bFGF-treated scars (p<0.01). Both maximal scar extension and the ratio of scar retraction to maximal scar extension, elasticity, by Cutometer were significantly greater in bFGF-treated scars than non-bFGF-treated scars (0.23 +/- 0.10 vs. 0.14 +/- 0.06 mm, 0.59 +/- 0.20 vs. 0.49 +/- 0.15 mm: scar extension, scar elasticity, bFGF vs. non-bFGF, p<0.01). The durometer reading was significantly lower in bFGF-treated scars than in non-bFGF-treated scars (16.2 +/- 3.8 vs. 29.3 +/- 5.1, p<0.01). Transepidermal water loss, water content, and corneal thickness were significantly less in bFGF-treated than in non-bFGF-treated scars (p<0.01).
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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".