IPEM topical report: guidance for the use of linac manufacturer integrated quality control
Bibliographic record
Abstract
Abstract This report provides guidance for users of linear accelerator (linac) manufacturer integrated quality control (MIQC) tools. MIQC tools have been developed and introduced by radiotherapy linac vendors, and have the potential to improve both the quality and efficiency of linac quality control (QC). They usually utilise the Electronic Portal Imaging Device (EPID), but may acquire data from other sources, and automatically perform and analyse tests of various treatment machine QC parameters. The currently available systems meeting this definition are Varian machine performance check, CyberKnife automated quality assurance /end-to-end, TomoTherapy Quality Assurance, and Elekta machine QA (also known as AQUA). This guidance report covers the commissioning and implementation of MIQC. The guidance has been developed by a radiotherapy special interest group working party on behalf of the Institute of Physics and Engineering in Medicine. Recommendations within the report are derived from the experience of the working party members, existing guidance, literature, and a United Kingdom survey conducted in 2022 (Pearson et al 2023 Phys. Med. Biol. 68 245018). Topics covered include developing an understanding of the QC system, independence review of MIQC, commissioning, implementation, ongoing QC and calibration, software upgrades and periodic review. The commissioning section covers detector commissioning, repeatability and reproducibility, baseline and tolerance setting, concordance with existing QC, sensitivity testing, cost-benefits analysis, and risk assessment methods. In order to offer practical guidance, case studies covering each aspect of commissioning are included. They are real-world examples or experiences from early adopters, each applied to a different example MIQC system. The examples will be directly applicable to users of that specific MIQC system, but also provide practical guidance on clinical implementation to users of the other systems.
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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 | 0.031 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.062 | 0.071 |
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".