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Record W4387526312 · doi:10.1016/j.jcmg.2023.09.005

Carotid Plaque-RADS

2023· article· en· W4387526312 on OpenAlexaff
Luca Saba, Riccardo Cau, Alessandro Murgia, Andrew Nicolaides, Max Wintermark, Maurício Castillo, Daniel Staub, Stavros K. Kakkos, Qi Yang, Kosmas I. Paraskevas, Chun Yuan, Myriam Edjlali, Roberto Sanfilippo, Jeroen Hendrikse, Elias Johansson, Mahmud Mossa‐Basha, Niranjan Balu, Martin Dichgans, David Saloner, Daniël Bos, Hans Rolf Jäger, Ross Naylor, Gavino Faa, Jasjit S. Suri, Justin Costello, Dorothee P. Auer, J. Scott McNally, Leo H. Bonati, Valentina Nardi, Aad van der Lugt, Maura Griffin, Bruce A. Wasserman, M. Eline Kooi, Jonathan H. Gillard, Giuseppe Lanzino, Dimitri P. Mikhailidis, Daniel M. Mandell, John C. Benson, Dianne H.K. van Dam-Nolen, Anna Kopczak, Jae W. Song, Ajay Gupta, J. Kevin DeMarco, Seemant Chaturvedi, Renu Virmani, Thomas S. Hatsukami, Martin M. Brown, Alan R. Moody, Peter Libby, Andreas Schindler, Tobias Saam

Bibliographic record

VenueJACC. Cardiovascular imaging · 2023
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersAmerican Society of NeuroradiologyAmerican Heart Association
KeywordsMedicineVulnerable plaqueRadiologyCarotid arteriesModalitiesMagnetic resonance imagingStroke (engine)StenosisMedical physicsPathologyInternal medicine

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.065
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0650.039

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 head0.012
GPT teacher head0.243
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations185
Published2023
Admission routes1
Has abstractno

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