Adjuvant Radiotherapy After Keloid Excision
Bibliographic record
Abstract
BACKGROUND: Surgical excision with adjuvant radiotherapy has gained attention as an effective treatment of keloid. The Asian population is challenged with a high incidence of keloid occurrence with a specific genetic predominance. The annual reported incidence of new keloid cases in Taiwan is around 30,000, but the disease control rate and effectiveness by means of surgical excision with adjuvant radiotherapy is not yet clear. METHODS: A retrospective chart review of the included consecutive keloid patients receiving surgical excision and radiotherapy was performed from 2013 to 2016 in a single institute. The reported risk factors were collected to investigate according to the outcome analysis. The Vancouver Scar Scale and the Japan Scar Workshop (JSW) Scar Scale were used to evaluate the correlation with keloid recurrence. RESULTS: In this series, the overall recurrence rate was 32%, reported with an average follow-up of 28 months. Independent risk factors varied according to the different outcome variables. Only JSW classification score independently predicted the risk of keloid recurrence (odds ratio, 1.305; P = 0.02). Both the Vancouver Scar Scale and the JSW system showed a good correlation with keloid recurrence (correlation efficiency, 0.529 and 0.54; P = 0.0437 and 0.0165, respectively). CONCLUSIONS: This preliminary report revealed convincing evidence of feasibility and effectiveness of applying adjuvant radiotherapy after keloid excision in the Taiwanese population. A more delicate biological equivalent dose of radiotherapy with an effective local control should be considered to improve the final outcome.
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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) | 0.000 | 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".