Efficacy of Punch Excision Combined With Superficial X‐Ray for the Treatment of Keloids: A Single‐Center Retrospective Study
Bibliographic record
Abstract
Background: Keloid management remains a challenge for clinicians. Recently, punch excision and photoelectric technology have demonstrated promising clinical applications for managing keloids; however, few studies have examined the efficacy and safety of punch excision combined with superficial X‐ray for treating keloids. Objective: To investigate the efficacy and safety of punch excision combined with superficial X‐ray for treating keloid. Methods: In this retrospective study, we analyzed the clinical records of 60 patients with keloid scars who underwent punch excision combined with superficial X‐ray at our hospital, from April 2020 to April 2023. The Vancouver Scar Scale (VSS) and Patient and Observer Scar Assessment Scale (POSAS) 2.0 Patient Scale serve as the primary assessment tools to evaluate all patients, both prior to the initial treatment and one year after the completion of therapy. SPSS software was used for statistical analysis to assess keloid improvement. Results: In the 60 patients with keloids, varying degrees of improvement were observed. The VSS and POSAS scores recorded 1 year after treatment were significantly lower than the pretreatment scores ( p < 0.001). No severe adverse reactions were observed during treatment. Conclusion: The combination of punch excision and superficial X‐ray demonstrated a notable therapeutic effect on keloids without evident adverse reactions, offering a safe and effective option for patients with keloids. Trial Registration: Chinese Registry of Clinical Trials: ChiCTR2400094289
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".