Poster ‐ 29: A Review of Patient Specific QA to Enable Program Comparisons Across Centers
Bibliographic record
Abstract
Purpose: To aid comparison of the performance of patient specific QA (PSQA) programs across centers Methods: Up to 414 VMAT/IMRT plans from various treatment sites (most commonly prostate, prostate bed, lung and lung SBRT) from a year period of ArcCHECK measurements were analysed using a range of acceptance criteria including variations of the following parameters: Gamma or DTA, Relative (RD) or Absolute Dose (AD), Van Dyk and Threshold (TH). A total of 24 different criteria were selected in the SNC Patient software to evaluate each plan and determine all reportable passing rates (PR). Results: The average passing rate (Rateavg) for all plans using all combinations of criteria ranges from about 93% using “3% Dose, 2mm DTA, AD, 10% TH, no Van dyk” criteria to nearly 100% using “Gamma with 3%Dose, 3mm DTA, RD, 10% TH, no Van dyk”. The 90th percentile of passing rates was also quantified and ranges from about 85% to nearly 100%. When analysed by site, prostate and prostate bed plans show the highest Rateavg, while SBRT lung plans shows the widest spread of rates. As expected, Rateavg decreases as the selection of Gamma changes to DTA, AD changes to RD, and “Van dyk” is turned off. Additionally, Rateavg decreases as TH percent selection decreases. Conclusions: The results enable comparisons of PR considering criteria stringency hence enabling better comparison of PSQA programs across centres. The study allows better understanding on how the parameters affect the reported PR and the level of stringency from all combinations.
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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 | 0.044 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".