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Record W2415014548 · doi:10.1017/s1460396916000285

Dosimetric impacts on skin toxicity for patients using topical agents and dressings during radiotherapy

2016· article· en· W2415014548 on OpenAlexaff
Karen Tse, Lyndon Morley, Angela Cashell, Annette Sperduti, Maurene McQuestion, James C. L. Chow

Bibliographic record

VenueJournal of Radiotherapy in Practice · 2016
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMedicineBolus (digestion)DosimeterNuclear medicinePolymethyl methacrylateSkin careSurgeryDosimetryMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Abstract Background and purpose Skin care practices for radiotherapy patients are complicated by dosimetric concerns. This study measures the effect on skin dose of various topical agents and dressings. Materials and methods Superficial doses were measured under 17 topical agents and dressings and three clinical materials for reference. Dose was measured using a MOSFET detector under a 1 mm polymethyl methacrylate slab, with 6 MV photon beams at 100 cm source to surface distance. Results Relative skin dose under reference materials was 128% (thermoplastic mask), 158% (5 mm bolus) and 171% (10 mm bolus). Under a realistic application of topical agent (0·5 mm), relative skin doses were 106–111%. All dry dressings yielded relative dose of ≤111%; two wet dressings yielded higher relative doses (133 and 141%). Conclusions Under clinically relevant conditions, no cream, gel or dry dressing increased the skin dose beyond that seen with a thermoplastic mask. Dressings soaked with water produced less skin dose than 5 mm bolus. This may be unacceptable if wet dressings are in place for the majority of the treatment course. Our results suggest that skin care practices should not be limited by dosimetric concerns when using a 6 MV photon beam.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.354
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2016
Admission routes1
Has abstractyes

Explore more

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