Efficacy of surgical resection and ultra-reduced tension suture combined with superficial radiation in keloid treatment
Bibliographic record
Abstract
BACKGROUND: There are many available treatment options for keloid; however, single treatments are usually less effective. Therefore, more scientifically rational and effective combined treatment methods should be sought to solve the pain associated with keloids. AIM: To explore the efficacy and safety of surgical resection and ultra-reduced tension suture combined with superficial radiation as keloid treatment. METHODS: Fifteen keloid patients admitted to Qingdao Eighth People's Hospital from June 2020 to January 2022 were enrolled in this retrospective analysis. All patients underwent a comprehensive treatment approach comprising surgical resection, ultra-reduced tension suture incision, and superficial radiation therapy within 24 h postoperatively. The modified Vancouver Scar Scale (mVSS) and Patient and Observer Scar Assessment Scale (POSAS) were used to evaluate the treatment effect, whereas the efficacy, adverse effects, and recurrence rate were observed according to the 12-mo follow-up after treatment. RESULTS: < 0.001), and the overall response rate was 93.3%. Only one case recurred, yielding a 6.7% recurrence rate. The incidence of local chromour sedimentation rate in 1-3 mo after radiotherapy was 33.3% (5 patients), all subsiding after 6-9 mo, without complications, such as delayed wound healing or dermatitis. CONCLUSION: Surgical resection, super subtraction sutures, and superficial radiotherapy are treatment methods with short courses, low recurrence rates, and good safety profiles.
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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) | 0.001 | 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".