Basic fibroblast growth factor is beneficial for postoperative color uniformity in split-thickness skin grafting
Bibliographic record
Abstract
Color changes of visible and exposed body surfaces, such as the face and extremities, after burn injury or surgery, such as skin grafting, flap, or sclerotherapy for vascular malformations, are sometimes a concern. The consequences reduce the satisfaction of both patients and physicians. An easy and reproducible method has not yet been established for an objective analysis of color changes; therefore, we tested a hand-held color analyzer (NF-333; Nippon Denshoku Co. Ltd) with data transport to a computer database and analysis software for posttreatment skin color change. The parameters included L, a, and b, which measure clarity, red, and yellow, respectively. Two groups were prospectively divided with 20 (11 females and nine males) patients per group. One group received skin grafting plus basic fibroblast growth factor (bFGF) spray daily and the other group received only skin grafting. The patients were randomized by the date of their first visit to our hospital. Patients were treated with bFGF on odd days, while patients who came on even days were included in the non-bFGF-treated group. The donor site for skin grafting was the lateral thighs and the thickness was similar in both groups. The results were compared at 1-year posttreatment follow-up. Clinical and objective assessments of the scars were performed 1 to years after complete healing. Color change differentials in comparison with the surrounding skin were lower with bFGF treatment in all parameters (p<0.01), along with clinical assessment with the Vancouver Scar Scale; therefore, the treatment contribute to a better color match with skin grafting postoperatively.
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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 | 0.001 |
| 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".