Prevalence and risk factors of MRI-defined brain infarcts among Chinese adults
Bibliographic record
Abstract
Background: Few studies have explored the prevalence and risk factors of brain infarcts (BI) detected by magnetic resonance imaging (MRI) in China. The purpose was to evaluate the prevalence and risk factors of brain infarcts (BI) detected by magnetic resonance imaging (MRI) in 1.4 million Chinese adults. Methods: This was a multicenter cross-sectional study conducted on 1,431,527 participants aged ≥18 years (mean age: 46.4 years) who underwent MRI scans in health examinations from 28 provinces of China in 2018. MRI-defined BI was defined as focal parenchymal lesions ≥3 mm. Multivariable logistic regression analyses were performed to evaluate risk factors associated with MRI-defined BI. Results: The age- and sex-standardized prevalence of MRI-defined BI, lacunar and non-lacunar infarcts were 5.79% (5.75-5.83%), 4.56% (4.52-4.60%), and 1.23% (1.21-1.25%), respectively. The sex-standardized prevalence of MRI-defined BI ranged from 0.46% among those aged 18-29 years to 37.33% among those aged ≥80 years. Men (6.30%) had a higher age-standardized prevalence of MRI-defined BI than women (5.28%). The highest age- and sex-standardized prevalence of MRI-defined BI was observed in the Northwest (8.34%) and Northeast (8.02%) regions, while the lowest prevalence was observed in the Southwest (4.02%). A higher risk of MRI-defined BI was associated with being male [odd ratio (OR) 1.17, 95% CI 1.15-1.19], older age (OR per 10-year increments 2.33, 2.31-2.35), overweight (1.12, 1.10-1.14) or obesity (1.18, 1.16-1.21), hypertension (1.80, 1.77-1.83), diabetes (1.24, 1.21-1.26), and dyslipidemia (1.07, 1.05-1.08). Conclusion: MRI-defined BI is highly prevalent in China, even among young adults. MRI-defined BI was associated with being male, older age, living in the northern region, and metabolic conditions.
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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) | 0.000 | 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) | 0.001 | 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".