A Single-Institution Review of the Use of Radiation in the Adjuvant and Definitive Management of Keloids
Bibliographic record
Abstract
AIMS: Many individuals suffer from keloids that are refractory to standard treatment modalities, including surgical excision alone. Radiation therapy can be used to reduce the risk of recurrent keloids post-operatively, as well as be used as primary treatment for keloids not amenable to surgical resection. The purpose of this study was to review our institutional experience of radiation therapy for keloid management. MATERIALS AND METHODS: A retrospective review of patients treated with radiation therapy for keloids between 2014 and 2020 at our institution was performed. RESULTS: A total of 70 keloids in 41 patients were treated. For the 55 keloids treated with post-operative radiation therapy (16Gy delivered in 2 fractions), 82.5% (33/40) of evaluable lesions did not recur. Among the 15 keloids treated with definitive radiation therapy (24Gy delivered in 3 fractions), 78.6% (11/14) of evaluable keloids showed complete flattening, and 14.3% (2/14) had partial flattening. Both acute and late toxicities were mild, with only a single instance of grade 3 toxicity (dermatitis). CONCLUSION: Our study confirms that radiation therapy has a role in reducing the risk of keloid recurrence post-operatively, and plays an important role in the definitive management of unresectable keloids.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".