Image Interpretation Session
Bibliographic record
Abstract
The Image Interpretation Session is an exciting, traditional event presented in the Sunday afternoon program at the annual meeting of the Radiological Society of North America. Since its establishment in 1939, this valuable teaching session brings together a distinguished panel of experts who stimulate and captivate the audience with 10 distinctive cases. This year’s cases include two of each in the categories of neuroradiology, musculoskeletal, thoracic, abdominal, and pelvic imaging. The cases selected are from the Memorial Sloan–Kettering Cancer Center, New York, the University of California San Francisco, and Kyoto University, Japan, and they reflect a disease mix often found in tertiary care centers. The panel brings together five internationally renowned radiologists who will illustrate their individual approaches to reading and analyzing imaging findings and how they combine their knowledge of radiology and medicine to arrive at a differential diagnosis. We would like to emphasize that success is a journey, not a destination. Image analysis with a clinically relevant differential diagnosis is essential in assisting and guiding patient care. This year’s Image Interpretation Session highlights the impact of image interpretation on patient management. Therefore, in addition to the differential diagnosis, emphasis will be placed on how the diagnosis affects patient care. We hope to make the session an example of “real-life” practice of radiology, in which the active participation of radiologists at tumor boards and clinical conferences is becoming a regular and valued occurrence. The Image Interpretation Session, including questions, answers, and discussion, will be available online in early 2003 through the RSNA Link (Education Portal and InteractEd [www.rsna.org/education/interactive/sunday_image/index.html]) and may be viewed for category 1 continuing medical education credit. © RSNA, 2002
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.678 | 0.454 |
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".