Clinical study on autologous split-thickness skin graft combined with photodynamic therapy for the treatment of keloid in the female pubic region
Bibliographic record
Abstract
OBJECTIVE: Keloid, marked by abnormal fibroblast activity and excessive collagen buildup, presents a therapeutic challenge due to their high recurrence rates. The pubic region, a sensitive anatomical area, is rarely discussed in keloid treatment literature. Our study demonstrates that autologous split-thickness skin grafting combined with topical photodynamic therapy(PDT) effectively prevents the recurrence of keloids in pubic region by radically eliminating inflammation and inhibiting fibroblast proliferation. METHODS: This study retrospectively analyzed 32 keloid patients treated with autologous split-thickness skin grafting and photodynamic therapy (surgery+PDT group) and compared them to a control group of 29 patients who underwent surgery alone (surgery only group). The improvement rate was evaluated at baseline (month 0) and at a 12-month follow-up (month 12) using the modified Vancouver Scar Scale (mVSS) and the Patient and Observer Scar Assessment Scale (POSAS) RESULTS: The study involved 61 patients. Both treatment groups exhibited significant enhancements in mVSS and POSAS scores; however, the combination therapy group showed a statistically significant improvement in POSAS scores at 12 months compared to the surgery-only group. CONCLUSION: Patients receiving both surgery and PDT showed a significantly higher improvement rate compared to those undergoing surgery alone.
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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".