The Efficacy of Combination Therapy Involving Excision Followed by Intralesional 5-Fluorouracil and Betamethasone, and Radiotherapy in the Treatment of Keloids: A Randomized Controlled Trial
Bibliographic record
Abstract
Background: Combined therapy for keloids is currently recommended. Surgery is one of the main options, but the measures to prevent recurrence after excision are still being explored. Objective: The randomized controlled study aimed at evaluating the efficacy of excision followed by intralesional low concentrations of 5-fluorouracil (5-FU)(12.5 mg/mL) and betamethasone. Methods: Sixty patients were randomly assigned to three groups. Patients in group A had excision followed by 5-FU and betamethasone intralesional injections, group B had 5-FU and betamethasone intralesional injections, and group C had excision followed by radiotherapy. Efficacy parameters were assessed from 8 to 12 months, including improvement on the Vancouver Scar Scale (VSS) and the Patient and Observer Scar Scale (POSAS), as well as side effects and recurrence. Trial registration number: ChiCTR2100046025. Results: After 4 months’ treatment, the improvement of the VSS and POSAS scores in group A was not different from that in group C ( P > 0.05) but was superior to that in group B ( P < 0.05); the pain and pruritus of the three groups were relieved more than 50%. After 8 to 12 months’ follow-up, there was no statistical difference in the incidence of side effects and recurrence among the groups ( P > 0.05). Conclusion: Excision followed by intralesional low concentrations of 5-FU (12.5mg/mL) with betamethasone is a safe and sustainable treatment for keloid, with no significant difference from excision followed by radiotherapy. Keywords: keloid, excision, 5-fluorouracil, betamethasone, radiotherapy
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".