Poster — Thur Eve — 24: Clinical application of the new dosimetry formalism for composite nonstandard beams
Bibliographic record
Abstract
Solid Water™ phantom. Two Farmer-type chambers, Exradin A12 and NE2571, and a smaller Exradin A1SL ionization chamber were cross-calibrated against a reference detector, the PTW micro liquid ion chamber (microLion), in the lowest dose gradient region in each IMRT QA field delivery. Based on the new dosimetry formalism, the clinical correction factor was measured in a fully-rotated delivery and a delivery at a single gantry angle, a collapsed delivery. For the calibrated Exradin A12, the measured dose with the clinical correction factor was compared with a calculated dose using Monte Carlo (MC) methods. The clinical correction factor deviated from unity by up to 2.4% and 3.7% in the fully-rotated and collapsed deliveries, respectively, depending on the dose distribution in the chamber collecting volume. For the Exradin A1SL, the correction factor was generally closer to unity due to the reduced dose gradient on the smaller collecting volume. In the fully-rotated delivery, the measured dose with the clinical correction factor is different from the MC-calculated dose to within 4%; while the discrepancy was greater, up to 8%, in the collapsed delivery due to the much heterogeneous dose distribution in the chamber collecting volume. This work proves that the suggested dosimetry technique is effective to improve the dosimetric consistency of clinical IMRT QA.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".