In ACS with planned invasive evaluation, prasugrel vs ticagrelor reduced a composite of death, MI, or stroke at 1 y
Bibliographic record
Abstract
ACP Journal Club21 January 2020In ACS with planned invasive evaluation, prasugrel vs ticagrelor reduced a composite of death, MI, or stroke at 1 yDominic L. Raco, MD, FRCPCDominic L. Raco, MD, FRCPCMcMaster University, Hamilton, Ontario, Canada, William Osler Health System, Brampton, Ontario, Canada (D.L.R.)Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/ACPJ202001210-005 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Source CitationSchüpke S, Neumann FJ, Menichelli M, et al. Ticagrelor or prasugrel in patients with acute coronary syndromes. N Engl J Med. 2019;381:1524-34. https://pubmed.ncbi.nlm.nih.gov/31475799Clinical Impact RatingsGIM/FP/GP: Hospitalists: Cardiology: Critical Care: References1 Yusuf S, Zhao F, Mehta SR, et al. Effects of clopidogrel in addition to aspirin in patients with acute coronary syndromes without ST-segment elevation. N Engl J Med. 2001;345:494-502. [PMID: 11519503] Google Scholar2 Bonaca MP, Bhatt DL, Cohen M, et al. Long-term use of ticagrelor in patients with prior myocardial infarction. N Engl J Med. 2015;372:1791-800. [PMID: 25773268] Google Scholar3 Ueda P, Jernberg T, James S, et al. External validation of the DAPT score in a nationwide population. J Am Coll Cardiol. 2018;72:1069-78. [PMID: 30158058] Google Scholar4 Wallentin L, Becker RC, Budaj A, et al. Ticagrelor versus clopidogrel in patients with acute coronary syndromes. N Engl J Med. 2009;361:1045-57. [PMID: 19717846] Google Scholar5 Wiviott SD, Braunwald E, McCabe CH, et al. Prasugrel versus clopidogrel in patients with acute coronary syndromes. N Engl J Med. 2007;357:2001-15. [PMID: 17982182] Google Scholar6 Valgimigli M, Bueno H, Byrne RA, et al. 2017 ESC focused update on dual antiplatelet therapy in coronary artery disease developed in collaboration with EACTS. Eur Heart J. 2018;39:213-60. [PMID: 28886622] Google Scholar Author, Article, and Disclosure InformationAffiliations: McMaster University, Hamilton, Ontario, Canada, William Osler Health System, Brampton, Ontario, Canada (D.L.R.)Disclosures: The commentator has disclosed no conflicts of interest. The form can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M19-2922.This article was published at Annals.org on 7 January 2020. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoIn stable CAD with type 2 diabetes, adding ticagrelor to aspirin reduced CV events but increased major bleeding Dominic L. Raco Metrics 21 January 2020Volume 172, Issue 2Page: JC5KeywordsAbsolute risk reductionAcute coronary syndromeAspirinHemorrhageIntent to treat analysisMortalityMyocardial infarctionPercutaneous coronary interventionSt segment elevation myocardial infarctionStroke ePublished: 21 January 2020 Issue Published: 21 January 2020 Copyright & PermissionsCopyright © 2020 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".