Treatment of keloids and hypertrophic scars using topical and intralesional mitomycin C
Bibliographic record
Abstract
BACKGROUND: Keloids develop due to the overgrowth of fibrous tissue. Currently, there is no gold standard treatment for keloids and hypertrophic scars (HTS). Their propensity for local invasion and recurrence has prompted many investigations on antineoplastic agents. OBJECTIVES: To investigate the efficacy of topical and intralesional mitomycin C for the treatment of keloids and HTS. METHODS: Nine patients with clinically diagnosed keloids and HTS were treated using topical mitomycin C (1 mg/mL) for 3 min after shaving excision. The Vancouver Scars Scale, patient satisfaction, and adverse effects were checked after 6 months. The keloids and HTS were photographed at each monthly visit. Intralesional mitomycin C (1 mg/mL) was administered to study the effect on the regression of keloids in 2 patients. RESULTS: Application of mitomycin C to the base of shave-removed keloids and HTS showed good results. Six out of 9 patients were very satisfied with the outcome of treatment; none were disappointed. The results of intralesional mitomycin C treatment were disappointing. Both cases worsened, with increased ulceration after treatment. CONCLUSIONS: Topical application of mitomycin C following shaving excision was safe and effective for the treatment of keloids and HTS. However, intralesional mitomycin C therapy aggravated both lesions.
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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".