Small composite field correction factors for the CyberKnife radiosurgery system: clinical and PCSR plans
Bibliographic record
Abstract
A formalism has been proposed for small and non-standard photon fields in which [Formula: see text] correction factors are used to correct dosimeter response in small fields (indiviual or composite) relative to that in a larger machine-specific reference (MSR) field. For clinical plans consisting of several fields, a plan-class specific reference (PCSR) plan can also be defined, serving as an intermediate calibration field between the MSR and clinical plans within a certain plan-class. In this work, the formalism was applied in the calculation of [Formula: see text] for 21 clinical plans delivered by the [Formula: see text] radiosurgery system, each plan employing one or two of the smallest diameter collimators: 5 mm, 7.5 mm, and 10 mm. Three detectors were considered: the Exradin A16 and A26 micro chambers, and the W1 plastic scintillator. The clinical plans were grouped into 7 plan-classes according to commonly shared characteristics. The suitability of using a PCSR plan to represent the detector response of each plan within the plan-class was investigated. Total and intermediate correction factors were calculated using the [Formula: see text] Monte Carlo user code. The corrections for the micro chambers were large, primarily due to the presence of the low-density air cavity and the volume averaging effect. The correction for the scintillator was found to be close to unity for most plans, indicating that this detector may be used to measure small clinical plan correction factors in any plan except for those using the 5 mm collimator. The PCSR plan was shown to be applicable to plan-classes comprising isocentric plans only, with plan-classes divided according to collimator size. For non-isocentric plans, the variation of [Formula: see text] as a function of the point of measurement within a single plan, as well as the high inter-plan-class variability of the correction factor, precludes the use of a PCSR 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".