The prevalence of mild cognitive impairment by aspects of social isolation
Bibliographic record
Abstract
OBJECTIVES: This study describes the prevalence of mild cognitive impairment (MCI) across different aspects of social isolation among adults 65 years or older. METHODS: In this cross-sectional study, we utilized the Wave 3 data from the National Social Life, Health, and Aging Project (NSHAP). MCI was defined as a Montreal Cognitive Assessment (MoCA) score less than 23. Prevalence of MCI was calculated for above and below average social disconnectedness (SD), perceived isolation (PI), and demographic variables age, gender, race/ethnicity, education, and household income. RESULTS: The overall prevalence [and 95% confidence interval] of MCI was 27.5% [25.5-29.6]. The high prevalence of MCI was found in those who had above average SD (32.0% [29.1-34.9]), above average PI (33.3% [29.7-36.8]), were older in age (43.1% [38.9-47.3]), male (28.7% [25.9-31.5]), Black (61.1% [52.5-69.6]), had less than a high school education (66.3% [58.9-73.8]), or were in the lowest income group (46.2% [39.7-52.7]). Those with above average SD or PI had a higher prevalence of MCI in almost all demographics, compared to those with below average SD or PI. Those who were Black or African American or had less than a high school education did not have a greater prevalence of MCI when SD was above average. DISCUSSION: This current study adds to the body of literature that links SD and PI to MCI and sheds light on the possible existing socio-demographic disparities. Groups with greater than average SD or PI tend to have a higher prevalence of MCI. Further studies are needed to establish a causal association of SD and PI with MCI.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".