SU‐F‐J‐49: IGRT Credentialing in NCTN Trials
Bibliographic record
Abstract
Purpose: To make Image Guided Radiation Therapy (IGRT) credentialing a more unified, consistent and efficient process across the entire National Clinical Trial Network (NCTN). Methods: IGRT plays a role in several advanced NCTN trials. Previously an institution had to be IGRT credentialed for each protocol. When institutions were allowed to use previous credentials for new protocols it was limited to the same disease site as the original credentialing. The credentialing was analyzed by the physics PI of the protocol. We consulted with several of these physicists to determine what is important to consider when reviewing submissions and to learn ways to apply credentialing more broadly. Results: For trials open in 2016, IGRT credentialing can be simplified to cover either boney anatomy or soft tissue. This revised credentialing will cover all disease sites based on the type of anatomy, unless otherwise stated within the protocol. Institutions will submit will complete an online questionnaire about their IGRT procedures. Boney anatomy requirements will include submission of data from 2 sequential fraction of both a patient aligned with boney anatomy and pelvic patient. Soft tissue will require similar submissions for a patient aligned using soft tissue and a pelvic patient. Institutions will only be required to submit the pelvic patient once. Data should be in DICOM format and includes planning CT set, RT structure set, RT plan file, RT dose file, localization images and spatial registration file (if available). Reviews will be done by IROC‐Houston staff who will continue to provide feedback to the sites. Conclusion: This revised IGRT credentialing process will bring consistency, a savings in time and effort for both the IROC Houston QA office and to those institutions wanting to be credentialed to participate in NCTN Trials. Sponsored by NIH/NCI CA10953
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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 | 0.316 | 0.420 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.063 | 0.048 |
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".