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Record W3012840589 · doi:10.1016/j.jacc.2020.01.044

2-Year Outcomes After Stenting of Lipid-Rich and Nonrich Coronary Plaques

2020· article· en· W3012840589 on OpenAlexaff
Myong Hwa Yamamoto, Akiko Maehara, Gregg W. Stone, Annapoorna Kini, Emmanouil S. Brilakis, David G. Rizik, Kendrick Shunk, Eric R. Powers, Jonathan M. Tobis, Brijeshwar Maini, Simon Dixon, James A. Goldstein, John L. Petersen, Philippe Généreux, Priti Shah, Aaron Crowley, Stephen J. Nicholls, Gary S. Mintz, James E. Muller, Giora Weisz

Bibliographic record

VenueJournal of the American College of Cardiology · 2020
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsHôpital du Sacré-Cœur de Montréal
Fundersnot available
KeywordsMedicineCoronary stentingInternal medicineCardiologyStentRestenosis

Abstract

fetched live from OpenAlex

BACKGROUND: Autopsy studies suggest that implanting stents in lipid-rich plaque (LRP) may be associated with adverse outcomes. OBJECTIVES: The purpose of this study was to evaluate the association between LRP detected by near-infrared spectroscopy (NIRS) and clinical outcomes in patients with coronary artery disease treated with contemporary drug-eluting stents. METHODS: was defined as the maximum LCBI within any 4-mm-long segment. Major adverse cardiac events (MACE) included cardiac death, myocardial infarction, definite or probable stent thrombosis, or unplanned revascularization or rehospitalization for progressive angina or unstable angina. Events were subcategorized as culprit (treated) lesion-related, nonculprit (untreated) lesion-related, or indeterminate. RESULTS: per 100: 1.06; 95% confidence interval: 0.96 to 1.17; p = 0.28) after adjusting clinical and procedural factors. CONCLUSIONS: Following PCI with contemporary drug-eluting stents, stent implantation in NIRS-defined LRPs was not associated with increased periprocedural or late adverse outcomes compared with those without significant lipid.

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)0.001
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 score0.006
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.016
GPT teacher head0.276
Teacher spread0.260 · 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

Citations22
Published2020
Admission routes1
Has abstractyes

Explore more

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