Dosimetric effect of collimating jaws for small multileaf collimated fields
Bibliographic record
Abstract
The dosimetric effects from the jaw positioned close to the small field (0.5 x 0.5, 1 x 1, and 2 x 2 cm2) side-edge generated by a single-focused multileaf collimator (MLC) were measured and studied. The measurement is important in intensity modulated radiotherapy (IMRT) because generally the jaw cannot perfectly cover all the leaf-ends in a segment of irregular field. This leads to additional dose contributed by (1) the end surface of the jaw, (2) the leaf-end, and (3) the inter- and intraleaf leakage/transmissions during the dosimetric measurement. Moreover, most of the conventional treatment planning systems ignore these effects in the dose calculation. In this study, measurements were made using a Varian 21 EX linear accelerator with 6 MV photon beam through a MLC containing 120 leaves. Percentage depth dose, beam profile, and output for small fields were measured by varying the jaw at different positions away from the leaf-ends in the field side-edge. Moving the jaw away from the leaf-ends increases the output and penumbra width for the small fields. Such increase is particularly significant when the field size is small (0.5 x 0.5 cm2) and the degree of increase changes quickly when the jaw-end is at about 1-2 cm from the leaf-end. It is suggested that measurements should be carried out in the IMRT commissioning to provide information to physicists in reviewing the treatment planning system's accuracy regarding leaf leakage/transmission and jaw effects.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".