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A Comparison of Stroke Risk Factors Between Men and Women with Disabilities

2006· article· en· W2076957212 on OpenAlexfundno aff
Janice L. Hinkle, Rosalind Smith, Karen Revere

Bibliographic record

VenueRehabilitation Nursing · 2006
Typearticle
Languageen
FieldMedicine
TopicAcute Ischemic Stroke Management
Canadian institutionsnot available
FundersReseau canadien de recherche respiratoireBristol-Myers Squibb
KeywordsStroke (engine)MedicineAffect (linguistics)GerontologyAlcohol consumptionPopulationPsychological interventionIncidence (geometry)Risk factorRehabilitationDiseaseDemographyPhysical therapyPsychologyEnvironmental healthPsychiatryAlcoholInternal medicine

Abstract

fetched live from OpenAlex

There are many adults with disabilities currently in the United States, yet little is known about how gender differences affect stroke risk factors in this population. This article presents a descriptive study that was designed to determine whether males and females living with disabilities differ in self-reported rates of stroke risk factors. Data were collected at conferences and meetings targeted for people living with disabilities. There were 146 participants; 54% were female; and the mean age was 58 years. The primary instrument was the Stroke Risk Screening tool. Stroke risk factors that differed significantly by gender include the incidence of hypertension (48% of men versus 32% of women), current smoking (30% men versus 4% women), history of heart disease (13% men versus 1% women), daily consumption of alcohol (10% men versus 1% women), and use of illicit drugs (10% men versus 0% women). Rehabilitation nurses should focus on earlier assessment of stroke risk factors and appropriate interventions, especially with men living with disabilities.

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 score0.075
Threshold uncertainty score0.397

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)0.0000.000

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 head0.014
GPT teacher head0.304
Teacher spread0.290 · 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

Citations14
Published2006
Admission routes1
Has abstractyes

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