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Record W4317869763 · doi:10.1161/strokeaha.122.041734

Stroke in Women and Unique Risk Factors

2023· article· en· W4317869763 on OpenAlexafffund
Cheryl Bushnell, Moira K. Kapral

Bibliographic record

VenueStroke · 2023
Typearticle
Languageen
FieldMedicine
TopicEndometriosis Research and Treatment
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Advancing Translational SciencesNational Institute of Neurological Disorders and StrokeMedical Research CouncilNational Institutes of HealthUniversity of TorontoPatient-Centered Outcomes Research Institute
KeywordsMedicineStroke (engine)Stroke riskRisk factorInternal medicineCardiologyIschemic strokeIschemia

Abstract

fetched live from OpenAlex

There has been a plethora of studies focused on female-specific risk factors and sex differences in stroke published in the past year. This article summarizes several of those novel studies which provide new knowledge about the field of stroke in women. The Nurses' Health Study II provides new data on the association between laparoscopically proven endometriosis and future stroke, accounting for the mediation effects of hysterectomy and oophorectomy. In a cohort of women from China, the relationship between hysterectomy, oophorectomy, and stroke is further clarified, accounting for the age at which the procedure is performed. The UK Biobank study provides new information on the relationship between oral contraceptive and hormone replacement therapy and stroke, with analytical techniques that focus on the timing of events related to duration of exposure. Finally, 2 new meta-analyses address the question of whether sex differences exist in the presentation of stroke symptoms.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.315
Teacher spread0.291 · 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
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

Citations13
Published2023
Admission routes2
Has abstractyes

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