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Record W2088908396 · doi:10.1088/0031-9155/49/24/005

The dose distribution of low dose rate Cs-137 in intracavitary brachytherapy: comparison of Monte Carlo simulation, treatment planning calculation and polymer gel measurement

2004· article· en· W2088908396 on OpenAlexaff
M Fragoso, P. A. Love, Frank Verhaegen, C Nalder, A.M. Bidmead, Martin O. Leach, S Webb

Bibliographic record

VenuePhysics in Medicine and Biology · 2004
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsMcGill UniversityMontreal General Hospital
FundersEngineering and Physical Sciences Research CouncilCancer Research Institute
KeywordsBrachytherapyMonte Carlo methodImaging phantomMaterials scienceHomogeneousDosimetryRadiation treatment planningPolymerNuclear medicineBiomedical engineeringDose ratePhysicsMedical physicsRadiation therapyMedicineMathematicsComposite materialRadiologyStatistical physicsStatistics

Abstract

fetched live from OpenAlex

In this study, the dose distribution delivered by low dose rate Cs-137 brachytherapy sources was investigated using Monte Carlo (MC) techniques and polymer gel dosimetry. The results obtained were compared with a commercial treatment planning system (TPS). The 20 mm and the 30 mm diameter Selectron vaginal applicator set (Nucletron) were used for this study. A homogeneous and a heterogeneous-with an air cavity-polymer gel phantom was used to measure the dose distribution from these sources. The same geometrical set-up was used for the MC calculations. Beyond the applicator tip, differences in dose as large as 20% were found between the MC and TPS. This is attributed to the presence of stainless steel in the applicator and source set, which are not considered by the TPS calculations. Beyond the air cavity, differences in dose of around 5% were noted, due to the TPS assuming a homogeneous water medium. The polymer gel results were in good agreement with the MC calculations for all the cases investigated.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.256
Threshold uncertainty score0.282

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.111
GPT teacher head0.406
Teacher spread0.295 · 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

Citations30
Published2004
Admission routes1
Has abstractyes

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