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Evaluation of Single-Field Electron Beams for Postmastectomy Radiotherapy

2012· article· en· W2077755616 on OpenAlexvenueno aff
Hiba Omer, Atef Suleiman

Bibliographic record

VenueJournal of Analytical Oncology · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsElectronCathode rayPhotonRadiation therapyRadiationMonte Carlo methodBeam (structure)PhysicsField (mathematics)Nuclear medicineAtomic physicsMedicineOpticsRadiologyNuclear physicsMathematicsStatistics

Abstract

fetched live from OpenAlex

Introduction:electron beams have been extensively used in postmastectomy radiotherapy due to its homogenous dose at the surface followed by sharp fall-off sparing the underlying tissue. Multiple electron fields or electron photon mix were the techniques commonly used. An old study reported the successful use of single-field electron beams with beam energy of 20 MeV. Yet the potential risks of the organs at risk were not clearly shown. Objectives:the objectives of this study were to assess the possibility of applying single-field electron beams in postmastectomy radiotherapy in terms of: the dose distribution in the target, the volume of organs that receive a certain threshold dose and the volume of organs that receive low doses of radiation. Materials and Methods: the Monte Carlo codes of EGSnrc were used to simulate electron beams of different energies and gantry angles. The resulting dose files were used by XSTING to generate dose volume histograms, which were used for evaluation. Results and Discussions: the target coverage was quite poor in most of the studied scenarios. Improving the target coverage was at the expense of irradiating the lung and heart with unacceptable dose values. Conclusion and Recommendations: Single field electron beams cannot be used for postmastectomy radiotherapy. Multiple electron fields or photon electron mix are necessary and need to be assessed.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.718
Threshold uncertainty score0.432

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.037
GPT teacher head0.392
Teacher spread0.355 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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