Sci‐Sat AM: Radiation Dosimetry and Practical Therapy Solutions ‐ 12: Suitability of plan class specific reference fields for estimating dosimeter correction factors for small clinical CyberKnife fields
Bibliographic record
Abstract
Purpose: The purpose of this work is to investigate the utility of plan class specific reference (PCSR) fields for predicting dosimeter response within isocentric and non‐isocentric composite clinical fields using the smallest fields employed by the CyberKnife radiosurgery system. Methods: Monte Carlo dosimeter response correction factors (CFs) were calculated for a plastic scintillator and microchamber dosimeter in 21 clinical fields and 9 candidate plan‐class PCSR fields which employ the 5, 7.5 and 10 mm diameter collimators. Measurements were performed in 5 PCSR fields to confirm the predicted relative response of detectors in the same field. Results: Ratios of corrected measured dose in the PCSR fields agree to within 1% of unity. Calculated CFs for isocentric fields agree within 1.5% of those for PCSR fields. Large and variable microchamber CFs are required for non‐isocentric fields, with differences as high as 5% between different clinical fields in the same plan class and 4% within the same field depending on the point of measurement. Non‐isocentric PCSR fields constructed to have relatively homogenous dose over a region larger than the detector have very different ion chamber CFs from clinical fields. The plastic scintillator detector has much more consistent response within each plan class but still require 3–4% corrections in some fields. Conclusions: While the PCSR field concept is useful for small isocentric fields, this approach may not be appropriate for non‐isocentric clinical fields which exhibit large and variable ion chamber CFs which differ significantly from CFs for homogenous field PCSRs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".