A Comprehensive Systematic Review and Meta-analysis on the Risk Factors of Stroke in Iranian Population
Bibliographic record
Abstract
BACKGROUND: There are limited data on vascular risk factors (VRFs) in low- and middle-income countries (LMICs). This meta-analysis was completed to summarize the existing evidence on stroke risk factors (SRFs) in the Iranian population. METHODS: An electronic literature search of the databases including PubMed, Embase, Web of Science, Scopus, Scientific Information Database (SID), Magiran, and IranMedex was performed to identify the related articles published up to March 2018. For categorical or continuous variables, the data were also pooled using the fixed- or the random-effect models, respectively, expressed as odds ratio (OR) or weighted mean difference (WMD). RESULTS: A total of 15 articles were recruited. The risk of stroke was associated with mean age, but not gender. Among traditional VRFs, hypertension (HTN), systolic and diastolic blood pressure (DBP), diabetes mellitus (DM), and fasting blood glucose (FBG) were associated with increased risk of stroke. Apart from the high circulating levels of triglycerides (TG), low-density lipoprotein-cholesterol (LDL-C), total cholesterol (TC), and low high-density lipoprotein-cholesterol (HDL-C), other potential risk factors namely cigarette smoking (CS), opioid addiction (OD), and waist circumference (WC) were identified to be independent stroke determinants. CONCLUSION: The present systematic review and meta-analysis provided a summary of the most important SRFs, which are potentially modifiable and preventable. Overall, Iran, similar to many other LMICs, is experiencing an ever-increasing rate of stroke-prone elderly people. The LMICs are thus suggested to develop national approaches to recognize and address VRFs, to monitor and control CS and OD rates, and to encourage a healthy lifestyle.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".