Poster — Thur Eve — 40: Dynamic arc sliding window tests for checking MLC gap consistency in rapid arc delivery
Bibliographic record
Abstract
MLC gap control is critical for dosimetric accuracy in rotational IMRT (RapidArc, VMAT) treatments. Systematic MLC gap change of 1 mm may cause 3-4% change of EUD to PTV for a typical H&N RapidArc plan. Therefore it is important to monitor MLC gap through QC procedures. For this purpose, we have created dynamic arc sliding window (SW) plans with fixed width MLC slits sliding across a jaw defined field. Plans with MLC slit widths of 5, 10, 15, and 20 mm, respectively, and the same length of 20 cm (in Y direction) were created with 6MV photons in a single arc of gantry angles from 182° to 178°. Dose delivered from these SW plans was measured using an ion chamber in a cylindrical phantom placed at isocentre, and values for dosimetric leaf gap (DLG) were derived based on relative dose measurements. DLG measured in dynamic arc SW tests agrees with that measured in fixed gantry angle SW fields to within 0.02 mm. We also extracted the MLC leaf gaps during MLC travels in these dynamic arc SW deliveries from MLC positions recorded in dynalog files, and compared to the MLC gaps in fixed gantry SW fields. We found that MLC leaf gaps were maintained excellently constant whether in dynamic arc or fixed gantry angle SW delivery, with typical standard deviation of MLC gaps of only ∼0.01mm for all involved leaf pairs. We believe these dynamic arc SW tests are very useful for checking MLC leaf constancy for RapidArc delivery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".