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Record W4404316472 · doi:10.1212/wnl.0000000000210087

Association of Vascular Risk With Severe vs Non-Severe Stroke

2024· article· en· W4404316472 on OpenAlexfundno aff
Catriona Reddin, Michelle Canavan, Graeme J. Hankey, Shahram Oveisgharan, Peter Langhorne, Xingyu Wang, Helle K. Iversen, Fernando Laņas, Raja Rizwan Hussain, Anna Członkowska, Aytekin Oğuz, Conor Judge, Annika Rosengren, Denis Xavier, Salim Yusuf, Martin O’Donnell

Bibliographic record

VenueNeurology · 2024
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Institutes of HealthPfizerCanadian Stroke NetworkHeart and Stroke Foundation of CanadaVetenskapsrådetBundesministerium für Bildung und ForschungDeutsche ForschungsgemeinschaftEuropean CommissionCanadian Institute for Theoretical AstrophysicsHealth Service ExecutiveWellcome TrustAstraZeneca
KeywordsStroke (engine)MedicineCardiologyInternal medicineAssociation (psychology)Stroke riskIschemic strokePsychologyIschemia

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: Acute stroke is associated with a spectrum of functional deficits. The objective of this analysis was to explore whether the importance of individual risk factors differ by stroke severity, which may be of relevance to public health strategies to reduce disability. METHODS: . RESULTS: < 0.001). DISCUSSION: Hypertension, atrial fibrillation, and smoking had a stronger magnitude of association with severe stroke (compared with non-severe stroke) while the increased waist-to-hip ratio had a stronger magnitude of association with non-severe stroke.

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)0.004
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.004
GPT teacher head0.216
Teacher spread0.211 · 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

Citations6
Published2024
Admission routes1
Has abstractyes

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