Three discipline collaborative radiation therapy (3DCRT) special debate: AI structure segmentation is <i>better</i> than clinician contouring for both OARs and targets
Bibliographic record
Abstract
Radiation Oncology is a highly multidisciplinary medical specialty, drawing significantly from three scientific disciplines-medicine, physics, and biology.As a result, discussion of controversies or changes in practice within radiation oncology involves input from all three disciplines, and sometimes more!For this reason, significant effort has been expended recently to foster collaborative multidisciplinary research in radiation oncology, with substantial demonstrated benefit.In light of these results, we endeavor here to adopt this "team-science" approach to the traditional debates featured in this journal.This article is part of the series of special JACMP debates entitled "Three Discipline Collaborative Radiation Therapy (3DCRT)" in which each debate team typically includes a radiation oncologist,a medical physi-
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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 | 0.039 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.020 | 0.027 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".