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Record W2111713259 · doi:10.1093/rpd/ncv166

Microdosimetric characteristics of 50 kV X rays at different depths for breast intraoperative radiotherapy

2015· article· en· W2111713259 on OpenAlexaff
Cheng‐Shie Wuu, Ren‐Dih Sheu, Jing Chen

Bibliographic record

VenueRadiation Protection Dosimetry · 2015
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsHealth Canada
Fundersnot available
KeywordsRadiation oncologyColumbia universityMedical physicsNuclear medicineLibrary scienceRadiation therapyMedicineComputer scienceRadiologySociologyMedia studies

Abstract

fetched live from OpenAlex

An intraoperative radiation therapy (IORT) device with 50 kV X rays was designed to deliver a single dose to the tumour bed after local excision of breast cancer. The quality of a radiation can be determined by the microscopic distribution of energy transfers along and across the charged particle tracks. The lineal energy, y, serves as an accurate measure of local energy concentration. The dose mean lineal energy, yD, is an indicator of radiation quality. For low linear energy transfer radiation, the ratio of its dose mean lineal energy to that of (60)Co gamma rays can serve as a good indicator of the relative biological effectiveness (RBE) at low doses. In this study, microdosimetric simulations are performed for soft tissue irradiated by 50 kV X rays generated from the IORT device, with a 4-cm breast applicator attached. All energy transfers are recorded with the location coordinates in the tissue. Microdosimetric single events in a sphere of 1 µm in diameter are scored as a function of radial distances from the applicator surface. Single-event spectra are then constructed. From those single-event spectra, dose mean lineal energy is calculated. Compared with dose mean lineal energy of (60)Co gamma rays, the estimated RBEs at low doses are given for the X rays at different depths in the tissue. The RBEs at clinically relevant doses, as a function of depth, are also presented.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.025
GPT teacher head0.278
Teacher spread0.253 · 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.

Study designBench or experimental
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

Citations0
Published2015
Admission routes1
Has abstractyes

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