The Efficacy of Bleomycin for Treating Keloid and Hypertrophic Scar
Bibliographic record
Abstract
Background: Keloids are benign fibroproliferative lesions characterized by abnormal collagen deposition within a skin injury. Keloid occurs as a result of an exaggerated tissue response to skin injury in a genetically-predisposed individual. Bleomycin is an anti-cancer agent that has been utilized for treating keloids and hypertrophic scars. It inhibits collagen synthesis and activates apoptosis of fibroblasts. Objective: To assess the effectiveness and the safety of bleomycin for treating keloids and hypertrophic scar. Patients and Methods: This was a prospective randomized experimental study, carried out on forty patients with keloid or hypertrophic scars. Dermatological examination included complete clinical assessment of lesions to determine the distribution, clinical variants and the extent of lesions. Assessment of keloid was done by Vancouver scar scale (VSS). The Patient and Observer Scar Assessment Scale (POSAS) were utilized to evaluate the efficacy of treatments. No recurrence was observed after six months follow up. Results: Sixty% of the patients were females. The commonest cause for lesions was surgery, there was a significant improvement in POSAS and VSS after treatment, 52.5% of the patients showed improvement percentage >75% and other 40% showed improvement percentage 50-75%, 50% of the patients had excellent satisfaction while 42.5% had good satisfaction, the most frequently reported adverse effect was hyperpigmentation. Conclusion: Bleomycin is a safe and effective method for treating keloids and 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 | 0.000 | 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) | 0.002 | 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".