Poster — Thur Eve — 74: A set of tests designed for electron dose calculation algorithm verification during a treatment planning system upgrade
Bibliographic record
Abstract
A set of tests were designed to verify an electron algorithm effectively and quickly during a treatment planning system upgrade. Based on TG‐53 report's suggestion and the assumption that the algorithm is well commissioned before the upgrade, the tests spot‐check the output factors, depth doses, off‐axis doses and treatment field sizes. The field sizes of 4×4, 6×6, 10×10, 15×15, 20×20 and 25×25 are to be tested. Four test plans are created for each field size, i.e., for open field, for extended SSD, for shaped field, and for bolus field. Fixed MU setting is recommended to avoid a possible plan normalization issue. The parameters to be recorded and compared include doses at dmax, R50 and Rp along central axis, which contain output and depth dose information, doses at four off‐axis points in dmax plane, which contain off‐axis dose and beam symmetry information, and FWHMs at dmax. For the plans other than open field only doses at dmax are checked. The tests were performed successfully during a planning system upgrade. The whole test can be completed in approximately 12 hours if the workload is distributed into multiple task carriers. It was found that most of the data agree very well between the old and the new version of the algorithm while some of the Rp or R50 doses deviated more than other data, which prompted a depth dose check. PDD comparisons were performed for the involved fields and it was found there were less than 0.5 mm PDD shifts occurred.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".