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Record W3161736931 · doi:10.1002/clc.23618

Contemporary use of <scp>guideline‐based</scp> higher potency <scp>P2Y12</scp> receptor inhibitor therapy in patients with <scp>moderate‐to‐high</scp> risk <scp>non‐ST‐segment</scp> elevation myocardial infarction: Results from the Canadian <scp>ACS</scp> reflective <scp>II cross‐sectional</scp> study

2021· article· en· W3161736931 on OpenAlexafffundabout
Ashish Patel, Shaun G. Goodman, Mary Tan, Neville Suskin, Robert S. McKelvie, Andrew Mathew, Sohrab Lutchmedial, Payam Dehghani, Andrea Lavoie, Thao Huynh, Shahar Lavi, Roger Philipp, Razi Khan, Andrew T. Yan, Sam Radhakrishnan, Tara Sedlak, Nathan W. Brunner, Hahn Hoe Kim, Tomas Cieza, S.A. Kassam, Christopher B. Fordyce, Michael Heffernan, Sean Jedrzkiewicz, Mina Madan, Shaheeda Ahmed, Colin Barry, Jean‐Pierre Déry, Akshay Bagai

Bibliographic record

VenueClinical Cardiology · 2021
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsHalTechOakville-Trafalgar Memorial HospitalUniversité LavalInstitut universitaire de cardiologie et de pneumologie de QuébecSt. Mary's UniversityUniversity of British ColumbiaHealth Sciences CentreRoyal Columbian HospitalLawson Health Research InstituteRegina General HospitalSaint John Regional HospitalLondon Health Sciences CentreSunnybrook Health Science CentreSt. Michael's HospitalUniversity of TorontoWestern UniversityMcGill University Health CentreCanadian Heart Research CentreSt Joseph's Health Care
FundersAstraZeneca CanadaAstraZeneca
KeywordsPrasugrelP2Y12PotencyMedicineTicagrelorClopidogrelMyocardial infarctionInternal medicinePharmacologyChemistryBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: After myocardial infarction, guidelines recommend higher-potency P2Y12 receptor inhibitors, namely ticagrelor and prasugrel, over clopidogrel. HYPOTHESIS: We aimed to determine the contemporary use of higher-potency antiplatelet therapy in Canadian patients with non-ST-elevation myocardial infarction (NSTEMI). METHODS: A total of 684 moderate-to-high risk NSTEMI patients were enrolled in the prospective Canadian ACS Reflective II registry at 12 Canadian hospitals and three clinics in five provinces between July 2016 and May 2018. Multivariable logistic regression modeling was performed to assess factors independently associated with higher-potency P2Y12 receptor inhibitor use at discharge. RESULTS: At hospital discharge, 78.3% of patients were treated with a P2Y12 receptor inhibitor. Among patients discharged on a P2Y12 receptor inhibitor, use of higher-potency P2Y12 receptor inhibitor was 61.4%. After adjustment, treatment in-hospital with PCI (OR 4.48, 95%CI 3.34-6.03, p < .0001) was most strongly associated with higher use of higher-potency P2Y12 receptor inhibitor, while oral anticoagulant use at discharge (OR 0.03, 95%CI 0.01-0.12, p < .0001), and atrial fibrillation (OR 0.40, 95%CI 0.17-0.98, p = .046) were most strongly associated with lower use of higher-potency P2Y12 receptor inhibitor. Use of higher-potency P2Y12 receptor inhibitor varied across provinces (range, 21.6%-78.9%). DISCUSSION: In contemporary Canadian practice, approximately 60% of moderate-to-high risk NSTEMI patients discharged on a P2Y12 receptor inhibitor are treated with a higher-potency P2Y12 receptor inhibitor. In addition to factors that increase risk of bleeding, interprovincial differences in practice patterns were associated with use of higher-potency P2Y12 receptor inhibitor at discharge. Opportunities remain for further optimization of evidence-based, guideline-recommended antiplatelet therapy use.

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 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.362

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.048
GPT teacher head0.313
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

Citations3
Published2021
Admission routes3
Has abstractyes

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