Sci-Thurs PM: Planning-09: Simulation of RapidArc Delivery Errors for Head and Neck Cancer
Bibliographic record
Abstract
RapidArc is a novel commercial delivery system that simultaneously varies the multi-leaf collimator (MLC), dynamic dose rate and gantry positions. Single arc 360° RapidArc plans were generated on Eclipse v8.6 with a prescription dose of 60 Gy (2.4Gy/fraction) for 3 head and neck cancer patients. Systematic and random errors were applied to the MLC (±0.5 mm, ±1 mm, ±2 mm), gantry positions (±0.25°, ±1°, ±2°) and MU per control point (±1.25%, ±2.5%, ±5%) by editing the DICOM plan file using an in-house MATLAB program. Erroneous DICOM plans were re-imported for dose calculation and compared to the original plan based on dose differences and 3D gamma analysis. For random errors, the maximum dose deviations for the three plans were 0.95±0.20Gy, 2.02±0.58Gy, 3.93±0.78Gy for 0.5, 1 and 2 mm errors in the MLC, 1.60±0.39Gy, 1.84±0.30Gy and 2.40±0.42Gy for the 0.25, 0.5 and 1° errors in gantry positions and 0.14±0.03Gy, 0.28±0.02Gy, 0.55±0.08Gy for 1.25, 2.5 and 5% errors in the MU per control point. For the systematic errors, the maximum dose deviations for the three plans were 3.72±0.87Gy, 7.31±1.67Gy, 14.42±3.09Gy for the 0.5, 1 and 2 mm errors in the MLC, 2.59±1.03Gy, 2.82±0.91Gy, 3.58±1.03Gy for the 0.25, 0.5 and 1° errors in the gantry and 0.97±0.16Gy, 2.10±0.49Gy, 3.96±0.57Gy for the 1.25, 2.5 and 5% errors in the MU per control point. For the delivery parameters and ranges of errors that were studied, systematic errors in the MLC positions produced the largest dose deviations to the final dose distribution as compared to the original plan.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".