Poster — Wed Eve—30: Routine QA and Re‐Commissioning of Linacs, TomoTherapy, and Treatment Planning Systems with MapCHECK™
Bibliographic record
Abstract
Recent developments in radiation delivery and treatment planning systems require more complicated quality assurance (QA) procedures. There are various QA test tools and specialized equipment for performing linear accelerator (Linac) and treatment delivery QA tests. However in order to have an effective QA program and ensure execution of the program, QA procedures should be simple and yet tailored so that each procedure checks as many facets as possible, while keeping the operating cost reasonable. At our center we use a 2D detector array, MapCHECK™ from SUN NUCLEAR Corporation, not only for its intended filmless IMRT QA but also for routine Linac and TomoTherapy machine QA as well as QA and commissioning of radiation treatment planning system. Using MapCHECK™ has improved our QA program feasibility and accuracy and has brought us one step closer to a paperless QA program.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.163 | 0.043 |
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".