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Record W2037886788 · doi:10.1118/1.4866223

Addendum to the AAPMˈs TG‐51 protocol for clinical reference dosimetry of high‐energy photon beams

2014· article· en· W2037886788 on OpenAlexaff
M McEwen, Larry A. DeWerd, Geoffrey S. Ibbott, D Followill, D. W. O. Rogers, S. M. Seltzer, Jan Seuntjens

Bibliographic record

VenueMedical Physics · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsMcGill UniversityCarleton UniversityNational Research Council Canada
FundersNational Cancer Institute
KeywordsAddendumConsistency (knowledge bases)DosimetryCalibrationMonte Carlo methodPhotonProtocol (science)Absorbed doseMedical physicsPhysicsComputer scienceComputational physicsNuclear medicineMathematicsOpticsStatisticsMedicineQuantum mechanics

Abstract

fetched live from OpenAlex

An addendum to the AAPM's TG-51 protocol for the determination of absorbed dose to water in megavoltage photon beams is presented. This addendum continues the procedure laid out in TG-51 but new kQ data for photon beams, based on Monte Carlo simulations, are presented and recommendations are given to improve the accuracy and consistency of the protocol's implementation. The components of the uncertainty budget in determining absorbed dose to water at the reference point are introduced and the magnitude of each component discussed. Finally, the consistency of experimental determination of ND,w coefficients is discussed. It is expected that the implementation of this addendum will be straightforward, assuming that the user is already familiar with TG-51. The changes introduced by this report are generally minor, although new recommendations could result in procedural changes for individual users. It is expected that the effort on the medical physicist's part to implement this addendum will not be significant and could be done as part of the annual linac calibration.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.110
Threshold uncertainty score0.368

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.029
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0030.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.1100.098

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 head0.385
Teacher spread0.348 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations308
Published2014
Admission routes1
Has abstractyes

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