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Record W4399783126 · doi:10.1177/17474930241264685

Tea and coffee consumption and risk of acute stroke: The INTERSTROKE Study

2024· article· en· W4399783126 on OpenAlexafffund
Andrew Smyth, Graeme J. Hankey, Peter Langhorne, Catriona Reddin, Danuta Ryglewicz, Annika Rosengren, Dennis Xavier, Michelle Canavan, Shahram Oveisgharan, Xingyu Wang, Patricio López‐Jaramillo, Albertino Damasceno, Anna Członkowska, Helle K. Iversen, Fernando Laņas, Salim Yusuf, Martin O’Donnell

Bibliographic record

VenueInternational Journal of Stroke · 2024
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsHamilton Health SciencesMcMaster UniversityPopulation Health Research Institute
FundersCanadian Institutes of Health Research
KeywordsMedicineStroke (engine)Odds ratioLogistic regressionConditional logistic regressionConfidence intervalBlack teaOddsGreen teaPopulationDemographyEnvironmental healthInternal medicineFood science

Abstract

fetched live from OpenAlex

BACKGROUND: Stroke is a leading global cause of death and disability. Daily tea/coffee intake is consumed by > 50% of populations and may represent an important population-level exposure. Therefore, it is first essential that we better understand the associations between the tea/coffee intake and stroke. AIMS: This research aims to generate hypotheses about the global associations between tea and coffee intake and stroke. These insights will identify interventions for stroke prevention that can be further explored using alternative study designs. METHODS: INTERSTROKE is a large international matched case-control study of first stroke from 32 countries. Participants were asked "how many cups do you drink each day?" of coffee, green tea, black tea, and other tea. Multivariable conditional logistic regression was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between intake and stroke. RESULTS: We included 13,462 cases and 13,488 controls from INTERSTROKE; mean age was 61.7 (13.4) years and 59.6% (n = 16,010) were male. Overall, 19.4% (n = 5239) did not consume tea/coffee, 47.0% (n = 12,666) consumed tea only, 14.9% (n = 4024) consumed coffee alone, and 18.6% (n = 5021) consumed both, with significant regional variations. After multivariable adjustment, there was no association between low/moderate coffee intake and stroke, but high consumption (> 4/day) was associated with higher odds of all stroke (OR = 1.37 (95% CI = 1.06-1.77)) or ischemic stroke (OR = 1.32 (95% CI = 1.00-1.74)). Tea consumption was associated with lower odds of all (OR = 0.81 (95% CI = 0.69-0.94) for highest intake) or ischemic stroke (OR = 0.81 (95% CI = 0.68-0.98) for highest intake). CONCLUSIONS: High coffee consumption was associated with higher odds of all or ischemic stroke; low-moderate coffee had no association with stroke. In contrast, tea consumption was associated with lower odds of stroke. These associations suggest that individuals consider avoiding high coffee consumption (⩾ five cups/day) to impact future stroke risk. DATA ACCESS STATEMENT: The design and rationale of INTERSTROKE was published previously. Individual participant data, or other documents are not available.

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)0.001
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 score0.122
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.024
GPT teacher head0.361
Teacher spread0.338 · 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

Citations9
Published2024
Admission routes2
Has abstractyes

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