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Record W2103643301 · doi:10.1120/jacmp.v16i5.5407

Optimum radiation source for radiation therapy of skin cancer

2015· article· en· W2103643301 on OpenAlexaff
Habib Safigholi, William Y. Song, Ali S. Meigooni

Bibliographic record

VenueJournal of Applied Clinical Medical Physics · 2015
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
FundersVarian Medical Systems
KeywordsBrachytherapyNuclear medicineRadiationRadiation therapyIrradiationLimitingDose rateExternal beam radiationMaterials scienceMonte Carlo methodMedicineMedical physicsPhysicsRadiologyOpticsMathematics

Abstract

fetched live from OpenAlex

Several different applicators have been designed for treatment of skin cancers, such as scalp, hand, and legs using Ir-192 HDR brachytherapy sources (IR-HDRS), miniature electronic brachytherapy sources (eBT), and external electron beam radiation therapy (EEBRT). Although, all of these methodologies may deliver the desired radiation dose to the skin, but the dose to the underlying bone may become the limiting factor for selection of the optimum treatment technique. In this project, dose to the underlying bone has been evaluated as a function of the radiation type, thickness of the bone, and thickness of the soft tissue on top of bone, assuming the same radiation dose delivery to the skin. These evaluations are performed using Monte Carlo (MC) simulation technique with MCNP5 code. The results of these investigations indicate that, for delivery of the same skin dose with a 50keV eBT, 4 MeV or 6 MeV EEBRT techniques, the average doses received by the underlying bones are 5.31, 2, or 1.75 times the dose received from IR-HDRS technique, respectively. These investigations indicate that, for the treatment of skin cancer condition with bone immediately beneath skin, the eBT technique may not be the most suitable technique, as it may lead to excessive bone dose relative to IR-HDRS and 6 MeV or 4 MeV electron beams.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.429

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 communication0.0000.000
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 head0.082
GPT teacher head0.418
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 designOther design
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

Citations29
Published2015
Admission routes1
Has abstractyes

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