MétaCan
Menu
Back to cohort
Record W2321469487 · doi:10.1017/s1460396913000241

Evaluating the relevance of dosimetric considerations to patient instructions regarding skin care during radiation therapy

2013· article· en· W2321469487 on OpenAlexaff
Lyndon Morley, Angela Cashell, Annette Sperduti, Maurene McQuestion, James C. L. Chow

Bibliographic record

VenueJournal of Radiotherapy in Practice · 2013
Typearticle
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsProduct (mathematics)MedicineSkin careMedical physicsRelevance (law)Radiation doseNuclear medicineComputer scienceMathematicsNursing

Abstract

fetched live from OpenAlex

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.

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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.653
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.356
Teacher spread0.336 · 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

Citations11
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Radiotherapy in PracticeSame topicEffects of Radiation ExposureFrench-language works237,207