Sci‐Sat AM (2) Therapy‐09: Quality Audits of a Medical Physics QA Program
Bibliographic record
Abstract
Documented radiation incidents world‐wide illustrate the importance of a rigorous physics quality assurance (QA) program. Since the QA program is such an important part of a radiotherapy operation, it should itself be subject to quality audits to ensure its effectiveness. We have been tracking QA activities in a custom database application, and since 2000, have been auditing our QA activities every six months. The audit produces quality indicators of equipment QA and physicist chart‐checking. The compliance rate and effectiveness of our tests are tracked and the information is used to improve our processes. Attention to these indicators has led to improvements in on‐time compliance, which is now typically greater than 95% for QA tests and QA review. Chart‐check activities show a rate of potentially significant problems found of about 3%, which has remained consistent through changes in workload and through major changes in treatment planning software and processes. In this paper we will share some of the observations of QA performance data, and discuss how this data has been used to improve our QA program. We will present some general lessons to be learned about the effectiveness of QA programs, and the human psychology of doing this kind of activity well.
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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 | 0.018 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".