MétaCan
Menu
Back to cohort
Record W3182880598 · doi:10.1007/s10557-021-07213-y

Ticagrelor or Clopidogrel After an Acute Coronary Syndrome in the Elderly: A Propensity Score Matching Analysis from 16,653 Patients Treated with PCI Included in Two Large Multinational Registries

2021· article· en· W3182880598 on OpenAlexaff
Matteo Bianco, Alessandro Careggio, Carloalberto Biolè, Giorgio Quadri, Alicia Quirós, Sergio Raposeiras‐Roubín, Emad Abu‐Assi, Tim Kinnaird, Albert Ariza‐Solé, Christoph Liebetrau, Sergio Manzano‐Fernández, Giacomo Boccuzzi, Josè P.S. Henriques, A. Spirito, Christian Templin, Stephen B. Wilton, Lazar Velicki, Luís Cláudio Lemos Correia, Andrea Rognoni, Fabrizio Ugo, Iván J. Núñez‐Gil, Toshiharu Fujii, Alessandro Durante, Xiantao Song, Tetsuma Kawaji, Dimitrios Alexopoulos, Zenon Huczek, José Ramón González‐Juanatey, Shaoping Nie, Masa‐aki Kawashiri, Umberto Morbiducci, Alberto Domínguez‐Rodríguez, P. Destefanis, Alessia Luciano, Gaetano Maria De Ferrari, Ferdinando Varbella, Laura Montagna, Fabrizio D’Ascenzo, Enrico Cerrato

Bibliographic record

VenueCardiovascular Drugs and Therapy · 2021
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsLibin Cardiovascular Institute of Alberta
FundersAstraZeneca
KeywordsMedicineTicagrelorClopidogrelPercutaneous coronary interventionAcute coronary syndromeHazard ratioInternal medicineConventional PCIPropensity score matchingCardiologyPopulationProportional hazards modelIncidence (geometry)Confidence intervalMyocardial 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)0.002
metaresearch head score (Gemma)0.005
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.251
Teacher spread0.234 · 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

Citations14
Published2021
Admission routes1
Has abstractno

Explore more

Same venueCardiovascular Drugs and TherapySame topicAntiplatelet Therapy and Cardiovascular DiseasesFrench-language works237,207