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Record W1968481727 · doi:10.1088/0031-9155/52/12/015

A Monte Carlo study of the variation of electron fluence in water from a 6 MV photon beam outside of the field

2007· article· en· W1968481727 on OpenAlexaff
C.J.G. Kirkby, C. Field, M. Mackenzie, Alasdair Syme, B. G. Fallone

Bibliographic record

VenuePhysics in Medicine and Biology · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFluenceMonte Carlo methodPhotonElectronRADIUSBeam (structure)PhysicsCathode rayIrradiationRadiationImaging phantomOpticsField (mathematics)Atomic physicsComputational physicsMaterials scienceNuclear physicsMathematics

Abstract

fetched live from OpenAlex

Existing studies have suggested some debate on whether the quality of radiation that delivers dose outside of the primary field of a radiotherapy photon beam can be considered the same as that inside the primary field. We used a Monte Carlo approach to simulate the electron fluence differential in energy inside a water phantom in response to irradiation by a 6 MV photon beam. The goal was to quantify how significantly the electron fluence changes when moving from a volume exposed to the primary field to one outside of the primary field, and understand any potential biological implications. We scored the electron fluence outwards in annular volumes in response to a 5 cm radius 6 MV beam and at the central axis in response to a rectangular 6 MV beam partially blocked by an MLC. The resulting fluence spectra were compared to different low-LET sources for which biological response in the form of chromosomal aberrations has been published. Our results show a significant increase in the low energy component of the fluence spectra outside of the primary field, which increases the mean LET to values similar to that seen in response to a 137Cs photon source. In turn, it is shown that this has the potential to increase the RBE.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.334
Threshold uncertainty score0.999

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 head0.034
GPT teacher head0.356
Teacher spread0.322 · 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 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

Citations39
Published2007
Admission routes1
Has abstractyes

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