A case–control study on red meat consumption and risk of stroke among a group of Iranian adults
Bibliographic record
Abstract
OBJECTIVE: We aimed to examine the association between red meat consumption and stroke in a group of Iranian adults. DESIGN: A hospital-based case-control study. SETTING: The study included stroke patients and hospital-based controls. Usual dietary intakes of participants were assessed by means of a validated 168-item semi-quantitative FFQ. Total red meat consumption was calculated by summing up the consumption of red, processed and visceral meats. SUBJECTS: One hundred and ninety-five cases were stroke patients hospitalized in the neurology ward and 195 controls were recruited from patients hospitalized in other wards with no history of cerebrovascular diseases or neurological disorders. RESULTS: Participants with stroke were older, more likely to be male and less likely to be obese. Individuals in the highest tertile of red meat intake were 119 % more likely to have stroke (OR=2·19; 95% CI 1·33, 3·60) compared with those in the lowest tertile. After controlling for age, sex and total energy intake, the association between red meat consumption and stroke was strengthened (OR=2·72; 95% CI 1·53, 4·83). This association remained significant even after further controlling for physical activity and smoking as well as dietary intakes. Additional adjustments for BMI, diabetes, hypertension and hyperlipidaemia did not influence the association significantly (OR=2·51; 95 % CI 1·19, 5·09). CONCLUSIONS: Consumption of red meat was associated with greater odds of having stroke in a group of Iranian adults.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".