Incidence and risk factors associated with progression to mild cognitive impairment among middle aged and older adults.
Bibliographic record
Abstract
OBJECTIVE: We performed this longitudinal 2-year follow-up study to determine the incidence and risk factors associated with MCI in middle-aged and older adults. SUBJECTS AND METHODS: This community-based longitudinal study was conducted in adults aged ≥ 50 years with normal cognitive function in Shanghai community, China, over a period of two years. Information about the socio-demographic, behavioral, anthropometric, and biochemical parameters was obtained at the baseline and cognitive function was assessed at the end of the follow-up period using the Montreal cognitive assessment tool. RESULTS: A total of 985 participants aged ≥ 50 years were included in the analysis. Incidence of MCI during the 2-year follow-up period among the study participants was 26.7% (95% CI: 24.0%-29.6%). Participants with lower level of education [primary - adjusted RR=2.79 (95% CI: 1.38-5.64 and secondary - adjusted RR=1.62 (95% CI: 1.17-2.24)], with history of cerebral infarction (adjusted RR=1.49; 95% CI: 1.05-2.12), history of cerebral hemorrhage (adjusted RR=3.20; 95% CI: 1.22-8.40) were found to have significantly higher risk of MCI. Regular tea consumption was associated with significantly reduced risk of MCI development (adjusted RR=0.69; 95% CI: 0.49-0.96). CONCLUSIONS: Our study found that one in four participants developed MCI during the 2-year follow-up period. Lower educational level, history of cerebral infarction, cerebral hemorrhage and tea consumption were significant determinants of MCI incidence. The target groups identified in this study should be closely monitored with regular follow-up investigations for early diagnosis and appropriate management of the condition.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".