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Record W2913960890 · doi:10.1016/j.amjcard.2019.01.055

The Predictive Value of Coronary Artery Calcium Scoring for Major Adverse Cardiac Events According to Renal Function (from the Coronary Computed Tomography Angiography Evaluation for Clinical Outcomes: An International Multicenter [CONFIRM] Registry)

2019· article· en· W2913960890 on OpenAlexaff
Ji Hyun Lee, Asim Rizvi, Bríain ó Hartaigh, Donghee Han, Mahn Won Park, Hadi Mirhedayati Roudsari, Wijnand J. Stuijfzand, Heidi Gransar, Yao Lu, Tracy Q. Callister, Daniel S. Berman, Augustin DeLago, Martin Hadamitzky, Jöerg Hausleiter, Mouaz H. Al‐Mallah, Matthew J. Budoff, Philipp A. Kaufmann, Gilbert Raff, Kavitha M. Chinnaiyan, Filippo Cademartiri, Erica Maffei, Todd C. Villines, Yong-Jin Kim, Jonathon Leipsic, Gudrun Feuchtner, Gianluca Pontone, Daniele Andreini, Hugo Marques, Pedro de Araújo Gonçalves, Ronen Rubinshtein, Stephan Achenbach, Leslee J. Shaw, Benjamin J.W. Chow, Ricardo C. Cury, Jeroen J. Bax, Hyuk‐Jae Chang, Erica C. Jones, Fay Y. Lin, James K. Min, Jessica M. Peña

Bibliographic record

VenueThe American Journal of Cardiology · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of OttawaUniversity of British Columbia
FundersNational Institutes of HealthNational Heart, Lung, and Blood InstituteMinistry of Science and ICT, South KoreaMinistry of Science ICT and Future PlanningMichael Wolk Heart Foundation
KeywordsMedicineMaceInternal medicineRenal functionHazard ratioCardiologyInterquartile rangeMyocardial infarctionCoronary artery diseaseFramingham Risk ScoreProportional hazards modelPercutaneous coronary interventionConfidence intervalDisease

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 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)0.003
metaresearch head score (Gemma)0.001
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 score0.037
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
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 head0.036
GPT teacher head0.361
Teacher spread0.324 · 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

Citations20
Published2019
Admission routes1
Has abstractno

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