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Record W2240360006 · doi:10.1161/str.43.suppl_1.a146

Abstract 146: White Blood Cell Count and Risk of Stroke in Men and Women: the European Prospective Investigation into Cancer-Norfolk Prospective Population Study

2012· article· en· W2240360006 on OpenAlexaff
Yangmei Li, Xinxue Liu, Robert Luben, Amanda Adler, Nicholas J. Wareham, Kay‐Tee Khaw

Bibliographic record

VenueStroke · 2012
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsAdler
Fundersnot available
KeywordsMedicineStroke (engine)Prospective cohort studyQuartileHazard ratioPopulationInternal medicineIncidence (geometry)Cohort studyConfidence interval

Abstract

fetched live from OpenAlex

Background and Objectives: An elevated white blood cell (WBC) count has been reported to be associated with all-cause mortality and risk of cardiovascular diseases. While the relationship between leukocyte count and coronary heart disease has been well documented, evidence on the association with risk of stroke has been less consistent. The aim of this study was to investigate the relationship between WBC count and incidence of stroke in a large cohort of disease-free men and women, and to assess how far any associations might be explained by traditional risk factors for stroke. Methods: We examined the prospective association between full blood WBC count and incident stroke in 7,392 men and 9,049 women from the general population participating in the European Prospective Investigation into Cancer-Norfolk Study. Participants were aged 39-79 years, without known heart attack, stroke, and cancer at the baseline examination in 1993-1997 and were followed up for incident stroke till March 2008. Results: During the median follow-up of 12 years, 542 incident stroke cases were observed. The age- and sex- adjusted risk of incident stroke increased with the increase of WBC count. Compared to the lowest quartile of WBC count, the age- and sex- adjusted hazard ratios (HRs) and 95% CIs for stroke were 1.11 (0.86-1.45), 1.40 (1.10-1.79), and 1.65 (1.29-2.09) in the second, third, and fourth quartile, respectively. Adjusting for smoking attenuated the results, while further adjustment for socioeconomic and lifestyle risk factors changed the association very little. The association was further attenuated after adjustment for biological risk factors such as systolic blood pressure and a history of diabetes at baseline, but people with the highest quartile of WBC count still had a higher risk of stroke than those in the lowest quartile (HR 1.32, 95% CI 1.02-1.71). Every 2*10 9 /L increase in WBC count was associated with a hazard ratio of 1.14 (95% CI 1.02-1.26) for stroke in the fully-adjusted model which included age, sex, smoking status, BMI, social class, educational level, alcohol intake, physical activity, systolic blood pressure, a history of diabetes at baseline, and total serum cholesterol. Conclusions: A positive association between WBC count and stroke was observed in these middle-aged and older men and women. Adjustment for smoking attenuated the association while multivariate adjustment for other risk factors did not further change the results.

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)0.002
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.241
Teacher spread0.234 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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