Optimizing Radiotherapy for Keloids
Bibliographic record
Abstract
BACKGROUND: The high recurrence rate of keloids has lead to the use of multiple treatment adjuncts to improve cosmetic outcomes after surgery. To date, there has been no single, standardized modality agreed upon to produce the best results. The purpose of this study was to review the radiation-based treatments (brachytherapy, electron beam and X-ray) used for keloid management and compare their outcomes. METHODS: A literature review was performed from 1942 to October 2014 using the databases: PubMed database of the National Center of Biotechnology Information, MEDLINE, Biosis, Embase, Google scholar, and Cochrane database. Articles were reviewed for case numbers, patient demographics, keloid location, follow up, radiation modality, dose, keloid recurrence, and complications. RESULTS: A total of 72 studies met the inclusion criteria representing 9048 keloids. These studies were categorized by treatment: brachytherapy, electron, or X-ray. Meta-analysis demonstrated that radiotherapy after surgery had less recurrence when compared to radiotherapy alone (22% and 37%, respectively, P = 0.005). Comparison between modalities revealed that postoperative brachytherapy yielded the lowest recurrence rate (15%) compared with X-ray and electron beam (23% and 23%, respectively; P =0.04, P = 0.1). Subgroup analysis by location demonstrated chest keloids have the highest recurrence rate. The most commonly reported side effect of radiotherapy was changes in skin pigmentation. CONCLUSIONS: The results of this study reinforce postoperative radiotherapy as effective management for keloids. Specifically, brachytherapy was the most effective of the currently used radiation modalities.
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 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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".