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Record W4387414547 · doi:10.5114/jcb.2023.131781

Assessment of dose to vaginal mucosa for gynecologic template interstitial high-dose-rate brachytherapy using Monte Carlo simulation

2023· article· en· W4387414547 on OpenAlexafffund
Devin Van Elburg, Tyler Meyer, Kevin Martell, Sarah Quirk, Michael Roumeliotis

Bibliographic record

VenueJournal of Contemporary Brachytherapy · 2023
Typearticle
Languageen
FieldMedicine
TopicEndometrial and Cervical Cancer Treatments
Canadian institutionsUniversity of CalgaryBaker Hughes (Canada)
FundersCanadian Institutes of Health ResearchAlberta Innovates
KeywordsBrachytherapyMedicineUrologyGynecologyInternal medicineRadiation therapy

Abstract

fetched live from OpenAlex

Purpose: This study investigated reliable vaginal mucosa dose-volume histogram (DVH) metrics in gynecologic template interstitial high-dose-rate brachytherapy (HDR-BT) for the purpose of standardized dose reporting. Material and methods: . Clinical plans were re-calculated using Monte Carlo (MC) simulations both in heterogeneous material and in water. Results: , respectively. Conclusions: For perineal template gynecologic HDR-BT procedures, the 2 cc volume is the smallest representative volume that reliably reports vaginal dose and at minimum should be reported to establish dose and outcome evaluation.

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)0.000
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 score0.418
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.094
GPT teacher head0.404
Teacher spread0.310 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

Explore more

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