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Record W2806141704 · doi:10.1148/rg.265065926

Image Interpretation Session

2006· article· en· W2806141704 on OpenAlexaff
Anne C. Roberts, Andreas Adam, Christine B. Chung, Robert Cleveland, Ella A. Kazerooni, Robert D. Zimmerman

Bibliographic record

VenueRadiographics · 2006
Typearticle
Languageen
FieldMedicine
TopicRadiology practices and education
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineSession (web analytics)Interpretation (philosophy)RadiologyRadiological weaponMedical physicsComputer science

Abstract

fetched live from OpenAlex

The Image Interpretation Session, more informally known as the “Sunday film panel,” has been a yearly event at the Annual Meeting of the Radiological Society of North America since 1939. This year’s session will follow the tradition of bringing together five experts in radiology, each of whom will be shown two unknown cases. These experts represent the subspecialties of interventional radiology and abdominal imaging, musculoskeletal radiology, pediatric radiology, thoracic imaging, and neurologic imaging. The unknown cases have been assembled from generous contributions by radiologists throughout the United States. The selected cases come from Duke University, University of California San Francisco, Mayo Clinic, Massachusetts General Hospital, and University of California San Diego. Many other institutions also proffered cases, which allowed for a wealth of material from which to choose; unfortunately, only 10 cases can be selected. Many thanks to all who contributed material! The cases were generally chosen to provide a challenge to the experts and a learning experience for the audience. The forum of the film panel should not be viewed as a car race, with the spectators waiting for a crash. Instead, it represents an opportunity to observe expert radiologists as they analyze images and develop a differential diagnosis and then use that differential diagnosis to guide patient care. Radiologists must do more than simply describe the findings seen on images. In the current practice environment with highly sophisticated imaging equipment and techniques, we must make sure that we are not so entranced by the images that we forget they are tools for providing better patient care. We must use our experience to suggest appropriate diagnoses; to contribute to patient management; and, in the case of interventional radiologists, to intervene when necessary. The unknown cases will be shown not only at the RSNA annual meeting in McCormick Place, Chicago, but they will also be available online with streaming audio and images on the RSNA Web site. Radiologists from every region of the world can view these images and test their own abilities to analyze images, describe findings, develop a differential diagnosis, and intervene. The ability to communicate electronically allows us to share this experience. We invite the radiologists from around the globe to participate. © RSNA, 2006

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.299
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2006
Admission routes1
Has abstractyes

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