Poster — Thur Eve — 61: Intensity Modulated Radiation Therapy: The Relationship between Planar Dose Map Verification and Dosimetric Outcome
Bibliographic record
Abstract
This work analyses the consequences of random and systematic dose differences in the comparison of calculated against measured planar dose distributions for IMRT from the perspective of a surrogate of the treatment outcome: Equivalent Uniform Dose (EUD). In‐house software was developed to incorporate normally distributed errors in the fluence maps of 3 head and neck (H&N) and 3 prostate plans and simulate dose differences that appear randomly across the planar dose maps. The plans with random errors were grouped according to the following passing rates: 1) 90–95%, 2)85–90% and 3)80–85% during patient specific quality control. The passing criteria included a 3% absolute dose, 3 mm distance to agreement (DTA), and a minimum dose difference of 2 cGy. A systematic 1% dose error could also be incorporated by altering the MUs of each plan with random errors. The impact of random errors on the prescribed EUDs of H&N plans ranged from −2.7 to −1.3% for the CTV and −1.0 to 0.6 Gy for the OARs while in prostate plans they ranged from −1.6 to −0.6% for the CTV and −1.2 to −0.4 Gy for the OARs over the range examined. The criterion of 90% passing rate for 3% absolute dose and 3 mm DTA kept the effects of random errors within a dosimetric goal of 2% change in prescribed EUDs of the targets and 2 Gy for the OARs. Systematic errors, if present, may cause larger effects on clinical dosimetry while still meeting patient specific quality control tolerances.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".