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Record W2805117454 · doi:10.1016/j.jacc.2018.02.079

Coronary Atherosclerotic Precursors of Acute Coronary Syndromes

2018· article· en· W2805117454 on OpenAlexaff
Hyuk‐Jae Chang, Fay Y. Lin, Sang‐Eun Lee, Daniele Andreini, Jeroen J. Bax, Filippo Cademartiri, Kavitha M. Chinnaiyan, Benjamin J.W. Chow, Edoardo Conte, Ricardo C. Cury, Gudrun Feuchtner, Martin Hadamitzky, Yong‐Jin Kim, Jonathon Leipsic, Erica Maffei, Hugo Marques, Fabian Plank, Gianluca Pontone, Gilbert Raff, Alexander R. van Rosendael, Todd C. Villines, Harald G. Weirich, Subhi J. Al’Aref, Lohendran Baskaran, Iksung Cho, Ibrahim Danad, Donghee Han, Ran Heo, Ji Hyun Lee, Asim Rivzi, Wijnand J. Stuijfzand, Heidi Gransar, Yao Lu, Ji Min Sung, Hyung‐Bok Park, Daniel S. Berman, Matthew J. Budoff, Habib Samady, Leslee J. Shaw, Peter H. Stone, Renu Virmani, Jagat Narula, James K. Min

Bibliographic record

VenueJournal of the American College of Cardiology · 2018
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of British ColumbiaUniversity of Ottawa
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineHazard ratioCulpritInternal medicineCardiologyCoronary artery diseaseConfidence intervalStenosisAcute coronary syndromeVulnerable plaqueCoronary atherosclerosisIntravascular ultrasoundRadiologyMyocardial infarction

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.015
GPT teacher head0.280
Teacher spread0.265 · 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 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

Citations475
Published2018
Admission routes1
Has abstractno

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