Poster — Thur Eve — 47: Evaluation of the ArcCHECK device for commissioning and patient‐specific QA
Bibliographic record
Abstract
The most promising method of accurately verifying VMAT treatments is by direct dose measurement over the three dimensions of irradiated volume. ArcCHECK device (Sun Nuclear, Melbourne, FL) have the potential to detect delivery errors on the treatment machine due to mechanical problems resulting from gantry and MLC motion. The estimation of the dosimetric leaf gap (DLG) parameter for Varian MLC (Varian Medical Systems, Palo Alto, CA) was attempted using ArcCHECK. Finding the optimal DLG value for use in TPS requires a measuring device like ArcCHECK to be employed especially in highly intensity modulated fields. In addition, ArcCHECK was used to assess the effect of positional error of MLC leaf in a given VMAT plan. Patient-specific QA tests were performed using the ArcCHECK device. QA results of patient plans that failed considering portal dosimetry technique were reassessed with ArcCHECK measurements for IMRT plans. The preliminary test results and performance of the ArcCHECK device were very encouraging. VMAT plans for head and neck cases were generated and their delivery was evaluated using ArcCHECK. Results have shown a success rate greater than 90% in the quality assurance of individual plans. Optimal DLG value was detected using ArcCHECK. Also, the device showed enough sensitivity to identify failed QA plans. Moreover, MLC central leaf pair position offset in a VMAT plan of the order of 1mm was fairly distinguished by ArcCHECK measurements.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".