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Record W2026256733 · doi:10.1161/strokeaha.107.497271

Antiplatelet Therapy for Secondary Prevention of Noncardioembolic Ischemic Stroke

2008· review· en· W2026256733 on OpenAlexaff
Martin O’Donnell, Graeme J. Hankey, John W. Eikelboom

Bibliographic record

VenueStroke · 2008
Typereview
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineAspirinClopidogrelDipyridamoleStroke (engine)CardiologyInternal medicineTiclopidinePrasugrelAnesthesia

Abstract

fetched live from OpenAlex

For patients with ischemic stroke or transient ischemic attack caused by atherothromboembolism, immediate and long-term aspirin reduces the relative risk of recurrent stroke, MI, and death attributable to vascular causes. Oral anticoagulation is not more effective than aspirin. Long-term clopidogrel reduces the relative risk of stroke, MI, or vascular death by about 9% (0.3% to 16.5%) compared with aspirin. Any long-term benefits of clopidogrel combined with aspirin, compared with aspirin or clopidogrel alone, appear to be offset by increased major bleeding. The combination of aspirin and extended-release dipyridamole reduces the relative odds of stroke, MI, or vascular death by about 18% (odds ratio 0.82, 0.74 to 0.91) compared with aspirin alone without causing more bleeding. Cilostazole reduces the risk of stroke, MI, or vascular death by 39% compared to placebo. A large clinical trial comparing clopidogrel with the combination of aspirin and dipyridamole, in >20 000 patients with recent (<120 days) atherothrombotic ischemic stroke, is expected to report in 2008. Emerging antiplatelet therapies presently being evaluated for secondary prevention of atherothromboembolism include other P(2)Y(12) ADP receptor antagonists (prasugrel, cangrelor, AZD 6140), thromboxane receptor antagonists (eg, S18886 - terutroban), and thrombin receptor (PAR-1) antagonists (eg, SCH530348).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.006
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.323
Teacher spread0.288 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations45
Published2008
Admission routes1
Has abstractyes

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