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Record W4388851049 · doi:10.1088/1361-6560/ad0eb3

IPEM topical report: results of a 2022 UK survey on the use of linac manufacturer integrated quality control (MIQC)

2023· article· en· W4388851049 on OpenAlexaff
Michael Pearson, Michael P. Barnes, Kirstie F Brown, Karen Hawthorn, Simon W Stevens, Rakesh Kizhakke Veetil, S. Weston, JR Whitbourn

Bibliographic record

VenuePhysics in Medicine and Biology · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsTomotherapyCyberknifeQuality (philosophy)Medical physicsLinear particle acceleratorQuality assuranceControl (management)MedicineComputer scienceOperations managementRadiosurgeryRadiation therapyEngineeringSurgeryPhysicsArtificial intelligenceBeam (structure)

Abstract

fetched live from OpenAlex

In recent years Radiotherapy linear accelerator (linac) vendors have developed their own integrated quality control (QC) systems. Such manufacturer-integrated-quality-control (MIQC) has the potential to improve both the quality and efficiency of linac QC but is currently being developed and utilised in the absence of specific best-practice guidance. An Institute of Physics and Engineering in Medicine working party was commissioned with a view to develop guidance for the commissioning and implementation of MIQC. This study is based upon a survey of United Kingdom (UK) radiotherapy departments performed by the working party. The survey was distributed to all heads of radiotherapy physics in the UK and investigated availability and uptake, community beliefs and opinions, utilisation, user experience and associated procedures. The survey achieved a 95% response rate and demonstrated strong support (>95%) for its use and further development. MIQC systems are available in 79% of respondents' centres, and are in clinical use in 66%. The most common MIQC system was Varian MPC, in clinical use in 58% of responding centres, with CyberKnife AQA\E2E in 11%, TomoTherapy TQA in 8% and no users of Elekta Machine QA. A majority of users found their MIQC to be easy to use, reliable, and had five or more years of experience. Most users reported occasions of discrepancy in results between MIQC and conventional testing, but the majority considered this acceptable, indicating a false reporting frequency of quarterly or less. MIQC has shown value in preventative maintenance and early detection of machine deviations. There were inconsistent approaches in the utilisation and commissioning tests performed. Fewer than half of users perform QC of MIQC. 45% of responders have modified their QC processes with the introduction of MIQC, via replacement of conventional tests or reduction in their frequency. Future guidance is recommended to assist in the implementation of MIQC.

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)0.004
metaresearch head score (Gemma)0.023
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 score0.022
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.006

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 head0.271
GPT teacher head0.437
Teacher spread0.166 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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