An algorithm for independent verification of Gamma Knife™ treatment plans
Bibliographic record
Abstract
A formalism for independent treatment verification has been developed for Gamma Knife radiosurgery in analogy to the second checks being performed routinely in the field of external beam radiotherapy. A verification algorithm is presented, and evaluated based on its agreement with treatment planning calculations for the first 40 Canadian Gamma Knife patients. The algorithm is used to calculate the irradiation time for each shot, and the value of the dose at the maximum dose point in each calculation matrix. Data entry consists of information included on the plan printout, and can be streamlined by using an optional plan import feature. Calculated shot times differed from those generated by the treatment planning software by an average of 0.3%, with a standard deviation of 1.4%. The agreement of dose maxima was comparable with an average of -0.2% and a standard deviation of 1.3%. Consistently accurate comparisons were observed for centrally located lesions treated with a small number of shots. Large discrepancies were almost all associated with dose plans utilizing a large number of collimator plugs, for which the simplifying approximations used by the program are known to break down.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".