Carbonated Beverage, Fruit Drink, and Water Consumption and Risk of Acute Stroke: the INTERSTROKE Case-Control Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Cold beverage intake (carbonated drinks, fruit juice/drinks, and water) may be important population-level exposures relevant to stroke risk and prevention. We sought to explore the association between intake of these beverages and stroke. METHODS: INTERSTROKE is an international matched case-control study of first stroke. Participants reported beverage intake using food frequency questionnaires or were asked "How many cups do you drink each day of water?" Multivariable conditional logistic regression estimated odds ratios (OR) and 95% confidence intervals (CI) for associations with stroke. RESULTS: We include 13,462 cases and 13,488 controls; mean age was 61.7±13.4 years and 59.6% (n=16,010) were male. After multivariable adjustment, carbonated beverages were linearly associated with ischemic stroke (OR 2.39 [95% CI 1.64-3.49]); only consumption once/day was associated with intracerebral hemorrhage (ICH) (OR 1.58 [95% CI 1.23-2.03]). There was no association between fruit juice/drinks and ischemic stroke, but increased odds of ICH for once/day (OR 1.37 [95% CI 1.08-1.75)] or twice/day (OR 3.18 [95% CI 1.69-5.97]). High water intake (>7 cups/day) was associated ischemic stroke (OR 0.82 [95% CI 0.68-0.99]) but not ICH. Associations differed by Eugeographical region-increased odds for carbonated beverages in some regions only; opposing directions of association of fruit juices/drinks with stroke in selected regions. CONCLUSION: Carbonated beverages were associated with increased odds of ischemic stroke and ICH, fruit juice/drinks were associated with increased odds of ICH, and high water consumption was associated with reduced odds of ischemic stroke, with important regional differences. Our findings suggest optimizing water intake, minimizing fruit juice/drinks, and avoiding carbonated beverages.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".