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Record W3202012131 · doi:10.1016/j.jacr.2021.07.015

Data Sharing of Imaging in an Evolving Health Care World: Report of the ACR Data Sharing Workgroup, Part 2: Annotation, Curation, and Contracting

2021· article· en· W3202012131 on OpenAlexaff
Juan Batlle, Keith J. Dreyer, Bibb Allen, Tessa S. Cook, Christopher J. Roth, Andrea Borondy Kitts, Raym Geis, Carol C. Wu, Matt P. Lungren, Jay Patti, Adam Prater, Daniel L. Rubin, Safwan S. Halabi, Mike Tilkin, Tom Hoffman, Laura P. Coombs, Christoph Wald

Bibliographic record

VenueJournal of the American College of Radiology · 2021
Typearticle
Languageen
FieldMedicine
TopicDigital Radiography and Breast Imaging
Canadian institutionsArtificial Intelligence in Medicine (Canada)
FundersNational Institute of Biomedical Imaging and BioengineeringNational Academy of MedicineUniversity of ChicagoJohnson and JohnsonAbbVieRadiological Society of North AmericaNational Institutes of HealthAid for Cancer ResearchMedtronicAmerican Lung AssociationAmerican College of PhysiciansAmerican College of Radiology Imaging NetworkGenentechPhilipsInternational Association for the Study of Lung CancerAssociation of American Medical CollegesAmerican Thoracic SocietyAmerican Cancer Society
KeywordsWorkgroupData sharingGeneral partnershipHealth careInformation sharingHealth Insurance Portability and Accountability ActComputer scienceInternet privacyData scienceBusinessKnowledge managementMedicineConfidentialityComputer securityPolitical scienceWorld Wide WebLaw

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.244
metaresearch head score (Gemma)0.231
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Open science
Consensus categoriesMetaresearch
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.933

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2440.231
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0140.024
Science and technology studies0.0090.005
Scholarly communication0.0110.011
Open science0.0100.016
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.041
GPT teacher head0.348
Teacher spread0.307 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReproducibility
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

Citations7
Published2021
Admission routes1
Has abstractno

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Same venueJournal of the American College of RadiologySame topicDigital Radiography and Breast ImagingFrench-language works237,207