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Record W4205330965 · doi:10.1016/j.jcin.2021.11.005

Ticagrelor Monotherapy After PCI in High-Risk Patients With Prior MI

2022· article· en· W4205330965 on OpenAlexaff
Mauro Chiarito, Usman Baber, Davide Cao, Samin K. Sharma, George Dangas, Dominick J. Angiolillo, Carlo Briguori, David J. Cohen, Dariusz Dudek, Vladimír Džavík, Javier Escaned, Robert Gil, Christian W. Hamm, Timothy D. Henry, Kurt Huber, Adnan Kastrati, Upendra Kaul, Ran Kornowski, Mitchell W. Krucoff, Vijay Kunadian, Shamir R. Mehta, David J. Moliterno, E. Magnus Ohman, Keith G. Oldroyd, Gennaro Sardella, Samantha Sartori, Giulio Stefanini, Richard Shlofmitz, Philippe Gabríel Steg, Giora Weisz, Bernhard Witzenbichler, Yaling Han, Stuart Pocock, C. Michael Gibson, Roxana Mehran

Bibliographic record

VenueJACC: Cardiovascular Interventions · 2022
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsHamilton Health SciencesUniversity Health Network
FundersSt. Jude MedicalEisaiSchool of Medicine, CHA UniversityBayer FundDuke Clinical Research InstituteDaiichi-SankyoMedicines CompanyBoston Scientific CorporationEli Lilly and CompanyBristol-Myers SquibbAstraZenecaCSL BehringChiesi FarmaceuticiPfizerCardiovascular Research FoundationScott R. MacKenzie FoundationRevanceBoehringer IngelheimAmgenGilead Sciences
KeywordsTicagrelorMedicinePercutaneous coronary interventionConventional PCIMyocardial infarctionAspirinInternal medicineCardiologyStroke (engine)Clinical endpointRandomized controlled trial

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.223
Teacher spread0.215 · 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 designNon-randomized trial
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

Citations11
Published2022
Admission routes1
Has abstractno

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