Process control analysis of IMRT QA: implications for clinical trials
Bibliographic record
Abstract
The purpose of this study is two-fold: first is to investigate the process of IMRT QA using control charts and second is to compare control chart limits to limits calculated using the standard deviation (sigma). Head and neck and prostate IMRT QA cases from seven institutions in both academic and community settings are considered. The percent difference between the point dose measurement in phantom and the corresponding result from the treatment planning system (TPS) is used for analysis. The average of the percent difference calculations defines the accuracy of the process and is called the process target. This represents the degree to which the process meets the clinical goal of 0% difference between the measurements and TPS. IMRT QA process ability defines the ability of the process to meet clinical specifications (e.g. 5% difference between the measurement and TPS). The process ability is defined in two ways: (1) the half-width of the control chart limits, and (2) the half-width of +/-3sigma limits. Process performance is characterized as being in one of four possible states that describes the stability of the process and its ability to meet clinical specifications. For the head and neck cases, the average process target across institutions was 0.3% (range: -1.5% to 2.9%). The average process ability using control chart limits was 7.2% (range: 5.3% to 9.8%) compared to 6.7% (range: 5.3% to 8.2%) using standard deviation limits. For the prostate cases, the average process target across the institutions was 0.2% (range: -1.8% to 1.4%). The average process ability using control chart limits was 4.4% (range: 1.3% to 9.4%) compared to 5.3% (range: 2.3% to 9.8%) using standard deviation limits. Using the standard deviation to characterize IMRT QA process performance resulted in processes being preferentially placed in one of the four states. This is in contrast to using control charts for process characterization where the IMRT QA processes were spread over three of the four states with none of the processes in the ideal state. Control charts may be used for IMRT QA in clinical trials to categorize process performance, minimize protocol variation and guide process improvements. For the duration of an institution's participation in a protocol, updated control charts can be periodically sent to the protocol QA center to document continued process performance to protocol specifications.
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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 | 0.571 | 0.793 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| 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".