IPEM topical report: results of a 2022 UK survey on the use of linac manufacturer integrated quality control (MIQC)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".