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Record W1925305330 · doi:10.1120/jacmp.v14i6.4239

Using mean dose rate to compare relative dosimetric efficiency with respect to source type and source change schedules for HDR brachytherapy°

2013· article· en· W1925305330 on OpenAlexaff
Stephen Davis, William Parker, Michael D. Evans

Bibliographic record

VenueJournal of Applied Clinical Medical Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsBrachytherapyDose rateRadioactive sourceSource modelDosimetryComputer scienceMedical physicsNuclear medicineRadiation therapyPhysicsMedicineRadiologyTelecommunications

Abstract

fetched live from OpenAlex

Remote afterloading devices used for high-dose-rate (HDR) brachytherapy may be supplied with different sources, and these sources typically have differing initial source strengths. In addition, the proposed frequency for source changes may also vary, depending upon the source type. Dosimetric parameters unique to each source are often used to compare source types. However, when considering the relative dosimetric efficiency between two HDR sources, the combined effect of source type, initial source strength, and source change scheme must be considered. A method of quantifying this combined effect by calculating mean dose rate from specific dosimetric source data is discussed. This method suggests an objective manner of comparing source scheme equivalency to facilitate performing a cost ratio analysis between different HDR sources and source change schemes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.071
GPT teacher head0.386
Teacher spread0.315 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2013
Admission routes1
Has abstractyes

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