Dosimetric verification of micro‐MLC based intensity modulated radiation therapy
Bibliographic record
Abstract
A methodology for the dosimetric verification of micro-multileaf collimator MMLC) based intensity modulated radiation therapy (IMRT) plans intended for stereotactic applications is described. The procedure is similar to that of conventional IMRT patient-specific quality assurance with some notable exceptions. Relative dosimetry measurements are performed with radiographic film, a commercial film-scanning system and a dose-image registration program. Film dosimetry results are within +/- 3.0% of calculated distributions or within 2.0 mm distance to agreement. Absolute dosimetry measurements are performed with a small volume ion-chamber and a commercially available stereotactic phantom. The cumulative dose from all beams is within +/- 2.0 % of the prescribed dose. Large deviations may be observed from individual beams since the smaller IMRT fields tend to have very few high-dose and low-gradient regions. An independent program that examines the treatment MLC file is used to estimate the central axis dose from each beam and provide a dose image that can be assessed alongside the intended fluence distribution prior to treatment. Tolerances for relative and absolute dosimetry of MMLC-based IMRT treatments are tighter than what is typically reported for conventional MLC-based IMRT. Also, the time commitment for the IMRT QA is slightly longer than of conventional MLC-based IMRT due to QA processes which check the mechanical alignment of the MMLC device with the laser and radiation isocentre.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".