Major Dietary Patterns Are Associated with Acute Ischemic Stroke
Bibliographic record
Abstract
Background: Dietary patterns play an essential role in vascular diseases; however, the association of specific dietary patterns and the risk of stroke is unknown. We designed the current study to evaluate several dietary patterns and their association with ischemic stroke. Materials and Methods: This case-control study was performed on 300 subjects, including 150 cases with ischemic stroke admitted to a tertiary referral center in Shiraz, Southern Iran, and 150 age- and sex-matched stroke-free individuals as controls. Using a 62-item Simple Stroke Food Frequency Questionnaire (SS-FFQ), we collected data regarding the dietary habits of all the participants. We extracted the major dietary patterns via principal component analysis using the varimax rotation technique with Kaiser Normalization. Finally, adherence to dietary patterns among the participants was divided into quartiles. We investigated the association between dietary patterns and ischemic stroke using multiple logistic regression analyses. Results: One-hundred and fifty ischemic stroke patients (91 males and 59 females, mean age of 63.9±16.04 years) and 150 age- and sex-matched controls (91 males and 59 females, mean age of 61.99±16.04 years) were included. After adjusting five major dietary patterns for vascular risk factors, we found that diets rich in fibers and plant-based proteins (P<0.001) and micronutrients (P<0.001) had a lower odds of ischemic stroke as compared to high-fat proteins (P=0.003) and empty calories (P<0.001) diets, but consuming healthy animal proteins (P=0.115) had no effects on the occurrence of ischemic stroke. Conclusion: Based on our study, we suggest that fibers and plant-based proteins, and micronutrient diets could reduce the odds of stroke, so public awareness about the effects of different dietary patterns should be raised.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".