WE‐E‐BRA‐05: Reducing the Frequency of Linac Output Check: A Statistical Model of Linac Output Fluctuation Based On a 3 Year History to Evaluate New Tolerances as a Function of Test Frequency for 12 Linear Accelerators
Bibliographic record
Abstract
Purpose: The study is based on a formalism to model the fluctuation of 12 linac outputs during a 3 year period. From a semi‐empirical statistical model and TG‐40 recommendations, a QA program is built from calculated action levels as a function of test frequency and time‐dependant probability distributions. Method and Materials: The linac output data is analyzed to fit a statistical model taking into account a systematic and a random component in the daily fluctuation. A 3 year history of daily output measurements totalizing 71 independent energies of photons and electrons is used to evaluate the parameters in each model. Action levels are calculated as a function of test frequency from obtained models based on tolerances defined in TG‐40. A confidence level of 95% is used to define the QA program such that machine output is kept within given limits, the latter being obtained by fitting tolerance functions with TG‐40 action levels. Measurement uncertainties are taken into account in the model and Gaussian statistics are used in the formalism. Results: Comparison between models and data history are in agreement with Gaussian statistics. For each linac and energy, tolerance functions are obtained from data history and new action levels are used with reduced test frequency. The tolerance of 2% recommended by TG‐40 for monthly output constancy check is reduced to values ranging from 1.2% to 1.8% for a test frequency of two months. Linac calibration frequency is reassessed to values ranging from 4 to 12 months. Conclusion: While daily check should be kept constant to prevent unpredictable variations, the frequency of output check can be reduced using new action levels based on a program‐defined limit and confidence level. Probability distributions can be used to evaluate linac calibration frequency based on machine stability. The model can be extended to other linac parameters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".