Intensity modulated arc deliveries approximated by a large number of fixed gantry position sliding window dynamic multileaf collimator fields
Bibliographic record
Abstract
The intensity modulated arc has been proposed as an alternative to tomotherapy. Treatment planing systems more typically model the conventional step and shoot or sliding window dynamic multileaf collimator (DMLC) deliveries, and may not support intensity modulated arc therapy (IMAT). As well, another potential drawback to this technique is that increasing the number of intensity levels required to achieve certain dose distributions necessitates increasing the number of gantry passes, as may occur if the desired dose distribution is complex (e.g., concave or bifurcated), potentially increasing the overall treatment time. A technique is presented here for the delivery of tomotherapy like dose distributions in a single gantry pass by the use of a large number of fields modulated by a sliding window DMLC technique from fixed equally spaced gantry positions. This serves as a good approximation to either IMAT or tomotherapy deliveries. The planning of these fields is achieved using iterative filtered back projection. Measured results of deliveries of varying degrees of complexity on a homogeneous phantom are compared to desired distributions.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".