Characterizing ionization chamber dosimetry in inverse planned IMRT fields
Bibliographic record
Abstract
It is standard practice in radiation therapy to have two independent calculations for the number of monitor units used in patient treatment plans. The relationship between monitor units and absorbed dose to water is sophisticated for MLC-based IMRT. Verification measurements of absorbed dose to water with ionization chambers in MLC-based IMRT fields remains uncertain and the accuracy of computer modeling is limited by the physics assumptions used. In this thesis, point dose measurements from three cylindrical ionization chambers of different collecting volumes are evaluated against the CORVUS (NOMOS Corporation, Cranberry, PA) finite-size pencil beam algorithm and the PEREGRINE (NOMOS Corporation, Cranberry, PA) Monte Carlo calculation engine. After establishing the characteristics of the chambers and treatment planning system under various beam geometries, dynamic and step and shoot MLC deliveries were evaluated. Between detectors, the smallest volume chamber measured the greatest dose. Compared to measurements, CORVUS and PEREGRINE both underestimated the dose in IMRT fields by approximately 5%. On average PEREGRINE yielded better agreement than CORVUS by 2%.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".