Preliminary efficacy and safety analysis: 12-month results in 83 patients using a novel approach of widefield radiation therapy for extensive skin field cancerization with or without keratinocyte cancers
Bibliographic record
Abstract
PURPOSE: Evaluate the use of widefield radiation therapy (RT) in the management of extensive skin field cancerization (ESFC) with/without keratinocyte cancer (KC). METHODS: The National Dermatology Radiation Oncology Registry is a multidisciplinary collaboration (dermatologists and radiation oncologists). It captures disease description, prior therapies, radiation prescription, clinical effect, skin cosmesis scores, and toxicity data. This analysis included 12-month follow-up data on 89 treated fields from a subset of 83 patients. RESULTS: = 25) ESFC with KC. Recurrence (4/89 [5%]) and appearance of new lesions (10/89 [11%]) were minimal. Cosmetic outcome was excellent/good in 98% ESFC and 96% ESFC with KC. Grade 1-2 acute radiation dermatitis occurred in up to 80% of treated fields. The frequency of Grade 3 acute skin toxicities was low. CONCLUSIONS: Registry data demonstrate the potential for widefield RT to treat patients with significant skin pathology who have exhausted other therapies and require durable, minimally invasive treatment options. At 12 months, observed clinical success rates were higher than those reported for topical interventions for ESFC. Ongoing follow-up is required to determine longer term outcomes.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".