The Combination of the Mini-Punch Technique and Photodynamic Therapy for the Treatment of Mandibular Keloids and Hypertrophic Scars
Bibliographic record
Abstract
BACKGROUND: Mandibular keloids and hypertrophic scars can exert significant effects on the appearance of a patient. However, current treatments are not effective in all cases. Consequently, it is vital to identify a safe and effective treatment method. OBJECTIVE: To investigate the therapeutic effect of the mini-punch technique combined with photodynamic therapy (PDT) on mandibular keloids and hypertrophic scars. PATIENTS AND METHODS: Twenty patients with mandibular keloids and hypertrophic scars were enrolled, including 5 cases of keloids and 15 cases of hypertrophic scars, with a total of 40 lesions. The mini-punch technique was performed first, and then, PDT was conducted, once a week on 3 occasions in total. RESULTS: After 12 months of follow-up, 30 lesions had improved by more than 50%, thus achieving a good therapeutic effect. The Vancouver Scar Scale score of patients ranged between 8 and 12 points with a mean of 9.60 ± 1.09 points before surgery and between 2 and 9 points with a mean of 4.15 ± 2.05 points at 12 months after surgery. The mean Vancouver Scar Scale score after treatment was significantly lower than that before treatment (t = 11.80, p < .001). CONCLUSION: A combination of the mini-punch technique and PDT is an effective treatment for mandibular 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".