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Record W2132239648 · doi:10.1517/14656566.2014.853039

An aspirin a day? Aspirin use across a spectrum of risk: cardiovascular disease, cancers and bleeds

2013· editorial· en· W2132239648 on OpenAlexaff
Michael R. Kolber, Christina Korownyk

Bibliographic record

VenueExpert Opinion on Pharmacotherapy · 2013
Typeeditorial
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineAspirinPrimary preventionDiseaseSecondary preventionIntensive care medicineContext (archaeology)Adverse effectPlatelet aggregation inhibitorPopulationInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Aspirin or acetylsalicylic acid (ASA) is commonly used in the general population for primary prevention of cardiovascular disease (CVD). Strong evidence supports the use of ASA in secondary prevention of CVD; however, for primary prevention, potential benefits are offset by potential harms (primarily major bleeds), with no benefit in overall mortality. Anti-platelet agents, including ASA, are one of the most commonly implicated medications for hospital admissions related to adverse medication events. Studies of primary prevention in patients with risk factors for CVD also fail to show a benefit with ASA. Finally, evidence supporting ASA use for cancer prevention is limited. Health care providers should be aware of the benefits and risks associated with ASA use in primary and secondary prevention and discuss these with their patients in the context of individual patient values and preferences.

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)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0040.004
Open science0.0030.001
Research integrity0.0140.015
Insufficient payload (model declined to judge)0.0090.011

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 head0.021
GPT teacher head0.339
Teacher spread0.318 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations12
Published2013
Admission routes1
Has abstractyes

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