Seasonal variations in measurements of linear accelerator output
Bibliographic record
Abstract
PURPOSE: Seasonal trends in linear accelerator output have been reported by at least one institution and data have suggested that they may be present at our center as well. The purpose of this work was to characterize these trends and determine whether local environmental conditions within the treatment rooms may be impacting the linear accelerators and/or the quality control (QC) dosimeter. METHODS: Runtime plots of daily output data, acquired using an in-house ion chamber-based device, over 3 yr and for 15 linear accelerators of different makes and models were reviewed and evaluated. Environmental conditions were monitored prospectively in a representative treatment room for approximately 9 months and evaluated for correlations with output trends. Independent measures of output using daily MV portal images were compared with output measurements using the ion chamber-based device. A separate controlled experiment probing the response of the in-house dosimeter to humidity changes over time was also carried out using a constant current source and a small enclosure. RESULTS: Runtime plots of output revealed sinusoidal, seasonal variations that were consistent across all treatment units, irrespective of manufacturer, model, or age of machine. The amplitude of the variation was on the order of 1% and maintained a yearly period. The independent measure of output using MV portal images did not corroborate the seasonal trends observed with the daily QC dosimeter. Based on the controlled experiment, the QC dosimeter was found to have a dependence on relative humidity changes, decreasing 1% in output per 30% increase in relative humidity. CONCLUSIONS: Results confirm the presence of underlying seasonal variations in measured output from the linear accelerators. The findings identify humidity impact on the measurement device as the underlying cause of the cyclical changes and not the accelerators themselves. These results could help minimize unwarranted machine servicing.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".