Bibliographic record
Abstract
Purpose: To investigate the possibility of extending the matrix detector for IMRT QA in Helical beam delivery Method and Materials: Matrix detectors, usually consisting of arrays of ion chambers or diodes, are a valuable tool for linac QA. A common method for IMRT is to irradiate the matrix with the detector plane perpendicular to the beam axis with each of the number of IMRT beams (typically 7 to 9) and the measured dose compared to the planned one. To evaluate the complete beam delivery, the individual distributions have to be combined. This is different from the standard means of using films for helical treatments as delivered by the Tomotherapy linac. In this work we want to investigate the possibility of extending the matrix detector for IMRT QA in such complex treatments. The proposed method is to configure the matrix as a phantom for setting up a delivery QA (DQA) plan, and then deliver the treatment plan beams on it as usual, taking care not to irradiate the electronics housing of the matrix. The detector plane will be designated in the DQA plan as the dose plane for comparison to the calculated one. A major concern is the angular dependency of the detectors on the direction of incidence of the radiation beams. This work aims to evaluate this limitation and to assess the usefulness of the device within the scope of this limitation Results: Preliminary measurements indicate quantitative agreement between calculated plan and measurements. More extensive results will be presented. Conclusion: The matrix detector has the potential to provide a fast QA tool for Tomotherapy and allows a dynamic view as radiation is being delivered. We propose to exploit this dynamic property to establish useful QA procedures to provide checks and analysis to a complex sequence of beam delivery.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".