Prioritizing clinical trial quality assurance for photons and protons: A failure modes and effects analysis (FMEA) comparison
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Global Clinical Trials RTQA Harmonization Group (GHG) set out to evaluate and prioritize clinical trial quality assurance. METHODS: The GHG compiled a list of radiotherapy quality assurance (QA) tests performed for proton and photon therapy clinical trials. These tests were compared between modalities to assess whether there was a need for different types of assessments per modality. A failure modes and effects analysis (FMEA) was performed to assess the risk of each QA failure. RESULTS: The risk analysis showed that proton and photon therapy shared four out of five of their highest-risk failures (end-to-end anthropomorphic phantom test, phantom tests using respiratory motion, pre-treatment patient plan review of contouring/outlining, and on-treatment/post-treatment patient plan review of dosimetric coverage). While similar trends were observed, proton therapy had higher risk failures, driven by higher severity scores. A sub-analysis of occurrence × severity scores identified high-risk scores to prioritize for improvements in RTQA detectability. A novel severity scaler was introduced to account for the number of patients affected by each failure. This scaler did not substantially alter the ranking of tests, but it elevated the QA program evaluation to the top 20th percentile. This is the first FMEA performed for clinical trial quality assurance. CONCLUSION: The identification of high-risk errors associated with clinical trials is valuable to prioritize and reduce errors in radiotherapy and improve the quality of trial data and outcomes, and can be applied to optimize clinical radiotherapy QA.
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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 | 0.201 | 0.251 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.009 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".