Optimizing the deliverability of binary collimation‐based SRS treatment for multiple metastases with multiple prescriptions
Bibliographic record
Abstract
intra-arc binary collimation (iABC) is a novel treatment technique in which dynamic conformal arcs are periodically interrupted with binary collimation. It has demonstrated its utility through planning studies for the treatment of multiple metastases. However, the binary collimation approach is idealized in the planning system, while the treatment deliveries must adhere to the physical limitations of the mechanical systems involved [e.g., multi-leaf collimation (MLC) leaf speed]. This work focuses on optimizing the delivery of the binary collimation-based stereotactic radiosurgery/radiotherapy (SRS/SRT) plans on a Varian TrueBeam accelerator, considering both dosimetric fidelity and treatment efficiency as variables. A transition window (TW) was defined as the fraction of a control point (CP) during which the requested MLC motion must be completed. The width of TWs was varied between 0% (or step-and-shoot which represents the idealized dose distribution), 20%, 40%, 60%, 80%, and 100%. A variable TW approach was also studied. Delivery accuracy was quantified with gamma analysis (gamma criteria 5%/2mm) on a PTW Octavius detector. The total beam-on-time was manually recorded. Smaller TWs were associated with more accurate dose deliveries and longer treatment delivery times. The variable TW method was found to be an effective compromise, achieving an average gamma pass rate of 98% and an average delivery time of 9 min.
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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) | 0.001 | 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".