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Record W3048917197 · doi:10.1002/acm2.12950

Long‐term experience of MPC across multiple TrueBeam linacs: MPC concordance with conventional QC and sensitivity to real‐world faults

2020· article· en· W3048917197 on OpenAlexaff
Michael Pearson, David Eaton, Tony Greener

Bibliographic record

VenueJournal of Applied Clinical Medical Physics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsTruebeamLinear particle acceleratorConcordanceTerm (time)Sensitivity (control systems)Positive predicative valueBeam (structure)Concordance correlation coefficientComputer sciencePredictive valuePhysicsMathematicsStatisticsOpticsMedicineEngineeringElectronic engineering

Abstract

fetched live from OpenAlex

Machine Performance Check (MPC) is an automated Quality Control (QC) tool that is integrated into the TrueBeam and Halcyon linear accelerators (Linacs), utilizing the imaging systems to verify the Linac beam and geometry. This work compares the concordance of daily MPC results with conventional QC tests over a 3-year period for eight Linacs in order to assess the sensitivity of MPC in detecting faults. The MPC output measurements were compared with the monthly ionization chamber measurements for 6 and 10 MV photon beams and 6, 9, 12, 16, and 18 MeV electron beams. All 6 MV Beam and Geometry (6MVBG) MPC test failures were analyzed to determine the failure rate and the number of true and false negative results, using the conventional QC record as the reference. The concordance between conventional QC test failures and MPC test failures was investigated. The mean agreement across 1933 MPC output and monthly comparison chamber measurements for all beam energies was 0.2%, with 97.8% within 1.5%, and a maximum difference of 2.9%. Of the 5000-6000 MPC individual test parameter results for the 6MVBG test, the highest failure rate was BeamOutputChange (0.5%), then BeamCenterShift (0.3%), and was ≤ 0.1% for the remaining parameters. There were 50 true negative and 27 false negative out of tolerance MPC results, with false negatives resolved by repeating MPC or by independent measurement. The analysis of conventional QC failures demonstrated that MPC detected all failures, except occasions when MPC reported output within tolerance, a result of the MPC-chamber response variation. The variation in MPC output versus chamber measurement indicates MPC is appropriate for daily output constancy but not for the measurement of absolute output. The comparison of the 6MVBG results and conventional records provides evidence that MPC is a sensitive method of performing beam and mechanical checks in a clinical setting.

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)0.010
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.380
Teacher spread0.351 · 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 designObservational
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

Citations21
Published2020
Admission routes1
Has abstractyes

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