A case–control study on egg consumption and risk of stroke among Iranian population
Bibliographic record
Abstract
BACKGROUND: Most available data that linked intake of egg to risk of stroke came from western countries, with conflicting findings. We aimed to examine the association between egg consumption and risk of stroke among Iranian adults. METHODS: In a hospital-based case-control study, 195 stroke patients, hospitalized in Alzahra University Hospital, were selected as cases and 195 control subjects, from patients hospitalized in other wards with no history of cerebrovascular diseases or neurologic disorders, were recruited. A validated 168-item food frequency questionnaire (FFQ) was used to assess participants' usual dietary intake, including egg consumption, over the previous year. Other required information was gathered by the use of questionnaires. RESULTS: Consumption of eggs was associated with lower odds of stroke, such that after adjustment for potential confounders, those in the highest category of egg intake (>2 eggs/week) were 77% lower odds to have stroke, compared with those with the lowest category of egg intake (<1 egg/week) (OR 0.23; 95% CI 0.11-0.45). Further controlling for body mass index strengthened the association (OR 0.20; 95% CI 0.09-0.41). CONCLUSIONS: We found evidence indicating that high intake of eggs (>2 eggs/week) during the past 1 year was associated with a lower risk of stroke. Further prospective studies are required to confirm these findings.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".