Role of vitamin D in treatment of keloid
Bibliographic record
Abstract
BACKGROUND: Keloid is a benign well-demarcated overgrowth of fibrotic tissue which extends beyond the original boundaries of a defect. The treatment of keloids is a particular challenge to dermatologists. Intralesional corticosteroid injection has been considered the first-line treatment for keloids. Vitamin D plays an important role in cell proliferation and differentiation as it slows the progression of tissue fibrosis by keloid fibroblasts and inhibits collagen synthesis in dermal fibrosis. OBJECTIVES: To evaluate the efficacy of intralesional injection of vitamin D in the treatment of keloids, both clinically and ultrasonically. METHODS: Forty Egyptian patients with keloid scars were injected weekly with intralesional vitamin D with dose of 0.2 ml (200,000 IU) per 1 cm lesion. The keloid scars were evaluated with Vancouver Scar Scale (VSS) and by a high-resolution ultrasound using B mode before and after treatment, the patients received 3 to 4 sessions. RESULTS: There was statistically highly significant reduction in VSS after treatment with intralesional vitamin D injection (p value≤0.001). There was also statistically highly significant improvement in ultrasonic keloid scar thickness after treatment (P value ≤0.001). CONCLUSIONS: Intralesional vitamin D is an effective and safe method in treatment of keloid scars. Ultrasound is a useful method in assessing the improvement of keloids after treatment.
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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) | 0.002 | 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".