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Record W4310938181 · doi:10.1093/eurheartj/ehac515

Acute, periprocedural and longterm antithrombotic therapy in older adults

2022· article· en· W4310938181 on OpenAlexaff
Felicita Andreotti, Tobias Geisler, Jean‐Philippe Collet, Bruna Gigante, Diana A. Gorog, Sigrun Halvorsen, Gregory Y.H. Lip, João Morais, Eliano Pio Navarese, Carlo Patrono, Bianca Rocca, Andrea Rubboli, Dirk Sibbing, Robert F. Storey, Freek W.A. Verheugt, Gemma Vilahur

Bibliographic record

VenueEuropean Heart Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineAntithromboticAtrial fibrillationAspirinClopidogrelIntensive care medicineStroke (engine)Acute coronary syndromeGuidelineMyocardial infarctionInternal medicineWarfarinCardiology

Abstract

fetched live from OpenAlex

The first international guidance on antithrombotic therapy in the elderly came from the European Society of Cardiology Working Group on Thrombosis in 2015. This same group has updated its previous report on antiplatelet and anticoagulant drugs for older patients with acute or chronic coronary syndromes, atrial fibrillation, or undergoing surgery or procedures typical of the elderly (transcatheter aortic valve implantation and left atrial appendage closure). The aim is to provide a succinct but comprehensive tool for readers to understand the bases of antithrombotic therapy in older patients, despite the complexities of comorbidities, comedications and uncertain ischaemic- vs. bleeding-risk balance. Fourteen updated consensus statements integrate recent trial data and other evidence, with a focus on high bleeding risk. Guideline recommendations, when present, are highlighted, as well as gaps in evidence. Key consensus points include efforts to improve medical adherence through deprescribing and polypill use; adoption of universal risk definitions for bleeding, myocardial infarction, stroke and cause-specific death; multiple bleeding-avoidance strategies, ranging from gastroprotection with aspirin use to selection of antithrombotic-drug composition, dosing and duration tailored to multiple variables (setting, history, overall risk, age, weight, renal function, comedications, procedures) that need special consideration when managing older adults.

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)0.000
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 score0.103
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.265
Teacher spread0.250 · 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

Citations57
Published2022
Admission routes1
Has abstractyes

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