Modifiable Factors Associated with Cognitive Impairment in 1,143 Japanese Outpatients: The Project in Sado for Total Health (PROST)
Bibliographic record
Abstract
BACKGROUND/AIMS: Evidence on modifiable factors associated with cognitive impairment in Japanese patients is scarce. This study aimed to determine modifiable factors for cognitive impairment in a Japanese hospital-based population. METHODS: Subjects of this cross-sectional study were 1,143 patients of Sado General Hospital (Niigata, Japan) registered in the Project in Sado for Total Health (PROST) between June 2008 and September 2014. We assessed disease history, body mass index (BMI), leisure time physical activity, walking time, smoking and drinking habits, and consumption of vegetables, fruits, and green tea as predictors, with cognitive impairment defined by the Mini-Mental State Examination (score <24) as an outcome. Multiple logistic regression analysis was performed to calculate odds ratios (ORs) for cognitive impairment. RESULTS: The mean subject age was 68.9 years, and the prevalence of cognitive impairment was 21.5%. Multivariate analysis revealed that age (p < 0.001), low BMI (<21.1; OR 1.39, 95% CI 1.12-1.72), a history of stroke (p = 0.003), a history of myocardial infarction (p = 0.038), low fruit consumption (p for trend = 0.012), and low green tea consumption (p for trend = 0.032) were independently associated with a higher prevalence of cognitive impairment. CONCLUSIONS: Modifiable factors, such as low BMI, low fruit consumption, and low green tea consumption, are associated with cognitive impairment. Longitudinal studies will be needed to confirm these findings.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".