Toward a Skin Dose-Area Metric Predictive of Moist Desquamation Using In Vivo Skin Dosimetry and Skin Assessments
Bibliographic record
Abstract
Background and Purpose : Moist Desquamation (MD) is a concerning acute side effect of radiation therapy for breast cancer, often seen in skin folds for patients having large or pendulous breasts. In-vivo skin dosimetry, clinical assessments and patient reported skin reactions were used to determine a relationship between dose-area metrics and the development of MD, to lend insight into skin tolerances and possibly guide future treatment planning dose constraints. Materials and Methods : Skin dose was measured using GafChromic TM film on the inner surface of an early prototype carbon-fiber accessory for breast support to remove the inframammary fold in 20 patients at high risk of developing MD undergoing adjuvant whole breast radiotherapy. Prescribed doses were 42.5 Gray (Gy) in 16 fractions or 50 Gy in 25 fractions using 6 to15 MV x-rays. To account for fraction size differences, analysis was performed using the equivalent dose in 2 Gy fractions using α/β = 11 (EQD2 11 ). MD was assessed out to 2 weeks post radiotherapy by trained therapists and by a patient reported outcome questionnaire. Results : Statistically significant differences in areas receiving 30 Gy to 48 Gy (EQD2 11 ) were observed between patients who did and did not develop MD in the infra-mammary area. Patients receiving EQD2 11 maximum dose ≤ 46Gy and ≥38Gy to ≤ 50cm 2 of their breast skin did not develop MD. Conclusions : The findings of this study offer insight into the relationship between skin toxicity and areas of skin irradiated to doses up to 50 Gy. Potential skin dose constraints to test in future studies to prevent MD are suggested.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".