Evaluating the relevance of dosimetric considerations to patient instructions regarding skin care during radiation therapy
Bibliographic record
Abstract
Abstract Introduction Patient teaching in radiation therapy may include restrictions on applying skin products owing to concerns that the presence of such materials may increase skin dose. These restrictions may create unnecessarily complicated and conflicting self-care instructions. Purpose To determine what thickness of skin product is necessary to produce a clinically meaningful dose increase to the skin, and provide recommendations for evidence-based patient instructions. Methods Dosimetric measurements and Monte Carlo simulations were used to calculate skin dose under 0–1·5 mm thicknesses of two common classes of skin product for a variety of treatment geometries. The thickness of product required to produce a clinically significant dose increase to the skin was determined. Results The thickness of product required to create a clinically meaningful dose increase was >0·7 mm for 10 × 10 cm 2 fields and >1·5 mm for 1 × 1 cm 2 fields. A typical application of product would be only 0·3 mm. Conclusion It seems unrealistic to anticipate patients using sufficiently large quantities of skin product to be of clinical concern. We therefore recommend that there are no dosimetric reasons to restrict the use of these types of skin products during radiation therapy for common treatment scenarios.
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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 | 0.020 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".