Systematic Review and Meta-analysis of Environmental Toxic Metal Contaminants and the Risk of Ischemic Stroke
Bibliographic record
Abstract
Background: Stroke is the second largest cause of mortality (WHO 2014) and long-lasting disability worldwide. Many risk factors are associated with stroke, such as age, gender, chronic illnesses, cardiovascular disease, lifestyle, and smoking. With global industrialization, the roles of environmental contaminants and their association with stroke are still unclear and have attracted much more attention. Materials and Methods: We conducted a systematic review on the environmental toxic metal contaminants and the risk of ischemic stroke. A comprehensive literature search was carried out till June 30, 2021 from databases such as PubMed, Science Direct, Embase, and Scopus. The quality of all the articles which met our inclusion criteria was assessed using Newcastle-Ottawa scaling, and four eligible studies were included for our systematic review. Results: The serum and urine cadmium concentrations were positively associated with the risk of ischemic stroke. There was an inverse association of serum and urine concentrations of mercury (Hg), serum concentration of gold and cerium with ischemic stroke, and the serum and urine concentrations of lead (Pb) had no association with ischemic stroke risk. Conclusion: The study showed strong associations between heavy metals and ischemic stroke, but more studies are required to prove the associations.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.025 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".