Bibliographic record
Abstract
In gated radiotherapy, the accuracy of treatment delivery is determined by the accuracy with which both the imaging and treatment beams are gated. If the time delays (the time between the target entering/leaving the gated region and the first/last image acquired or treatment beam on/off) for the imaging and treatment systems are in the opposite directions, they may increase the required internal target volume (ITV) margin, above that indicated by the tolerance for either system measured individually. We measured a gating system's time delay on 3 fluoroscopy systems, and 3 linear accelerator treatment beams, using a motion phantom of known geometry, varying gating type (amplitude vs. phase), beam energy, dose rate, and period. The average beam on imaging time delays were -0.04 +/- 0.05 s (amplitude, 1 SD), -0.11 +/- 0.04 s (phase); while the average beam off imaging time delays were -0.18 +/- 0.08 s (amplitude) and -0.15 +/- 0.04 s (phase). The average beam on treatment time delays were 0.09 +/- 0.02 s (amplitude, 1 SD), 0.10 +/- 0.03 s (phase); while the average beam off time delays for treatment beams were 0.08 +/- 0.02 s (amplitude) and 0.07 +/- 0.02 s (phase). The negative value indicates the images were acquired early, and the positive values show the treatment beam was triggered late. We present a technique for calculating the margin necessary to account for time delays and found that the difference between the imaging and treatment time delays required a significant increase in the ITV margin in the direction of tumor motion at the gated level.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".