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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.002 | 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 teacher head, 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".