Bibliographic record
Abstract
Abstract It has been suggested that modern radiation therapy could benefit from adopting the “End to End” (E2E) type of testing developed originally in computer science to determine whether applications and systems work as required under real-world scenarios. The motivation for adopting E2E techniques for image guided adaptive radiation therapy validation is to extend beyond current common testing using standard physics QA that inherently probes only select points or systems within the IGART schema. E2E methodologies extend the testing to evaluate complete IGART processes, including the complex interchanges that occur during and throughout a patient’s treatment as clinical staff interpret and respond to information acquired during the treatment course. While limited radiotherapy E2E QA may have been adopted periodically by clinics when implementing a new treatment technique, clinical E2E QA has been confined to date mainly to tests mediated by external auditing bodies such as IROC, the Imaging and Radiation Oncology Core in the United States. This testing often includes having the clinic in question irradiate a purpose-built phantom containing dosimeters to specific criteria under protocols set by the auditing body. The auditors then determine off site whether the clinic’s treatment process was successful by comparing the dose measurements with the intended dose delivery. The advance of three dimensional (3D) radiation dosimeters opens the possibility for in-house E2E testing. Approaches for in-house E2E testing have been proposed for over a decade, but such comprehensive internal E2E testing has not been widely adopted. In this presentation the barriers and challenges to the development of clinical in-house E2E QA will be reviewed primarily based on the experience in Kingston.
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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 | 0.015 | 0.021 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".