Poster — Thur Eve — 29: Detecting changes in IMRT QA using statistical process control
Bibliographic record
Abstract
Statistical process control (SPC) methods were used to analyze 239 measurement based individual IMRT QA events. The selected IMRT QA events were all head and neck (H&N) cases with 70Gy in 35 fractions, and all prostate cases with 76Gy in 38 fractions planned between March 2009 and 2012. The results were used to determine if the tolerance limits currently being used for IMRT QA were able to indicate if the process was under control. The SPC calculations were repeated for IMRT QA of the same type of cases that were planned after the treatment planning system was upgraded from Eclipse version 8.1.18 to version 10.0.39. The initial tolerance limits were found to be acceptable for two of the three metrics tested prior to the upgrade. After the upgrade to the treatment planning system the SPC analysis found that the a priori limits were no longer capable of indicating control for 2 of the 3 metrics analyzed. The changes in the IMRT QA results were clearly identified using SPC, indicating that it is a useful tool for finding changes in the IMRT QA process. Routine application of SPC to IMRT QA results would help to distinguish unintentional trends and changes from the random variation in the IMRT QA results for individual plans.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".