Gamma-Index Passing Rates in Baseline Plans Measured with a Detector Array
Bibliographic record
Abstract
Purpose: This study provides a simple protocol for validation of the gamma passing rates and to identify the optimum values of % dose and mm criteria for dose distributions measured with a detector array. Methods: We chose ArcCHECK detector array to illustrate the concepts. We used plans with uniform or quasi-uniform dose distributions along the detector array for testing in the presence of dose errors. For testing sensitivity to spatial shift we employed a plan with approximately constant dose gradient along the axis of the instrument. Results: We identified a representative set of parameters which describe performance of a detector array. We determined the minimum gamma-index acceptance criteria allowing the passing rates to reach 100% in the absence of errors, and identified the minimum fully detectable errors for such criteria. For our baseline plans delivered to ArcCHECK, 100% passing rates were obtained for 1.5% dose criterion together with ±3% minimum error detectable at 100% rate, and for 1.5 mm criterion together with the minimum fully detectable error of ±3 mm. We inspected the impact of selected program options on the passing rates. Conclusions: The protocol we developed provides a simple method of commissioning-style analysis of a detector array without a need for analysis of a large number of clinical 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".