Cognitive Test Performance in Relation to Health and Function in 12 European Countries: The SHARE Study
Bibliographic record
Abstract
BACKGROUND: Even subtle impairments on cognitive test scores can be associated with future cognitive decline and dementia. We assayed the relationships between test score impairment and adverse outcomes. METHODS: Secondary analyses were performed on data from non-institutionalized participants, 50+ years of age (N = 30,038), from 12 countries taking part in the Survey of Health, Ageing and Retirement in Europe (SHARE) longitudinal study on aging. At baseline, participants' cognition was tested using verbal fluency, immediate recall, and delayed recall tasks. RESULTS: Greater levels of cognitive impairment at baseline were strongly associated with future poor health outcomes and functional impairment. Controlling for age, sex and education, those with 1 (OR = 1.58, 95% CI = 1.34-1.87) or ≥ 2 (OR = 2.59, 95% CI = 2.17-3.09) impaired tests at baseline were more likely to die after an average of 40 months compared to individuals with no impairments. After selecting for participants who reported the absence of dementia initially, those with ≥ 2 cognitive impairments at baseline (OR = 3.34, 95% CI = 2.27-4.92) were more likely to report dementia at follow-up compared to those with no impairment. CONCLUSIONS: People with impaired cognitive test scores at baseline are at greater risk to die or develop dementia within four years than their less impaired or unimpaired counterparts.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".