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Record W2091046714 · doi:10.1088/0031-9155/53/24/008

Calculation of relative biological effectiveness of a low-energy electronic brachytherapy source

2008· article· en· W2091046714 on OpenAlexaff
Brigitte Reniers, Derek Liu, Thomas Rusch, Frank Verhaegen

Bibliographic record

VenuePhysics in Medicine and Biology · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsMcGill UniversityMontreal General Hospital
Fundersnot available
KeywordsBrachytherapyRelative biological effectivenessMonte Carlo methodNuclear medicineIsotopePhotonYield (engineering)PhysicsGamma rayLow energyRadiochemistryMedical physicsNuclear physicsRadiation therapyRadiationMedicineOpticsAtomic physicsChemistryRadiologyMathematicsStatistics

Abstract

fetched live from OpenAlex

Low-energy x-rays are known to have a higher relative biological effectiveness (RBE) than higher energy photons such as the gamma rays from 192Ir and 60Co. In this work the initial yield of single- and double-strand DNA breaks (SSB and DSB) and the RBE was estimated for a novel electronic brachytherapy source (EBS), emitting 40-50 kVp photons. An EGSnrc Monte Carlo model of the source was used in combination with the 'Monte Carlo damage simulation' program (Semenenko and Stewart 2004 Radiat. Res. 161 451-57; 2006 Phys. Med. Biol. 51 1693-706). The results indicate a substantially reduced SSB yield and increased DSB yield for the EBS compared to 60Co or 192Ir, leading to an enhanced RBE by 40-50%. The RBE estimate for the low-energy x-ray EBS was found to be very similar to the low-energy gamma ray brachytherapy isotope 125I. Biological damage was estimated in several human tissues: muscle, breast, calcified breast and cortical bone. SSB and DSB yields were similar in all media, except in bone. These findings should be taken into account if the EBS is intended to replace brachytherapy with the commonly used 192Ir isotope.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
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 head0.057
GPT teacher head0.357
Teacher spread0.301 · 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 designTheoretical or conceptual
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

Citations53
Published2008
Admission routes1
Has abstractyes

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