Dosimetric effects of incorrect jaw settings in cranial radiosurgery
Bibliographic record
Abstract
Abstract A small circular field used in stereotactic radiosurgery is obtained using a cone accessory attached to a LINAC. Such beam modification requires that the LINAC jaws be set to a specific size, an adjustment which, in many cases, must be explicitly made by the LINAC operator. Given that treatment planning systems are commissioned with correct jaw settings, it is difficult to quantify the leakage dose to the patient when the jaw setting is larger than recommended. However, there are documented cases indicating that the resulting overexposure can lead to long-term disability and death. This study quantifies the dose differences between correct and incorrect jaw settings by simulating a realistic treatment plan using a Varian LINAC with a BrainLab circular cone accessory. Using Monte Carlo methods, the details of the LINAC head and cone accessory were simulated, with calculated doses benchmarked against measurements. Calculations were based on a realistic treatment plan for right side trigeminal neuralgia delivering 90 Gy using a 6 MV (4 mm cone) beam with 7 arcs. The calculated doses to target and normal tissue with jaws set correctly (5 × 5 cm 2 ) and incorrectly (settings ranging from 10 × 10 cm 2 to 20 × 20 cm 2 ) were then compared. Incorrect jaw settings resulted in an increase in the target dose of less than 6%. This implies that pre-treatment QA based only on point dose measurements at the isocenter will not detect jaw setting errors. However, extremely high doses (>25 Gy) occurred 5 cm away from the isocenter. For example, dose to 50% of the volume, D 50 , in the eyes increased from 15 cGy with the correct jaw setting to 5318 cGy with an incorrect jaw setting of 20 × 20 cm 2 . D 50 in the brain increased from 27 cGy with the correct jaw setting to 4335 cGy for a setting of 20 × 20 cm 2 . The leakage radiation is shown to originate beyond the outer edge of the beam limiting cone (outer radius = 5 cm). Leakage dose can reach 15%, 40%, and 60% of target dose when incorrect jaw settings of 10 × 10 cm 2 , 15 × 15 cm 2 and 20 × 20 cm 2 are used in the beam delivery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".