Keloid Excision With Primary Closure Combined With Superficial Radiation Therapy (SRT-100)
Bibliographic record
Abstract
BACKGROUND: Surgery plus radiotherapy is associated with fewer recurrences after keloid treatment. However, the side effects of radiotherapy are of concern. Superficial radiation therapy has a low energy, targets the skin, and spares deeper structures, making it ideal for keloid treatment. Many studies have reported good outcomes after surgery combined with superficial radiation therapy. This study provided data on Taiwanese patients who underwent keloid excision with simple primary closure and superficial radiation therapy. METHODS: We retrospectively collected data from patients who underwent keloid excision with postoperative radiotherapy at our hospital. All patients underwent keloid excision and primary wound closure without Z-plasty or a local flap. Subsequently, patients underwent 2 or 3 fractions of superficial radiation therapy (SRT) on postoperative days 0, 1, and 2 (in 3 fractions). We collected data on the patients' preoperative Vancouver Scar Scale (VSS), 2-month follow-up VSS score, recurrence, and side effects. RESULTS: In total, 16 keloids in 12 patients were treated with excision, primary closure, and superficial radiation therapy. The mean preoperative VSS was 8.69 ± 1.79, whereas the mean 2-month postoperative VSS was 3.56 ± 0.70. Most of the keloids were followed up for more than 6 months. No keloid recurrence was observed. A side effect of radiotherapy is hyperpigmentation of the skin surrounding the surgical scar. CONCLUSIONS: Keloid excision with primary closure combined with postoperative SRT leads to a good outcome with no recurrence and a shorter incision wound that satisfies patients and reduces the complications of hyperpigmentation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".