Compression of cognitive morbidity by higher education in individuals aged 75+ living in Germany
Bibliographic record
Abstract
BACKGROUND: Previous studies have shown that higher education may reduce dementia risk and promote a better cognitive functioning in older age. OBJECTIVE: The study investigated to what extent higher education leads to compression of cognitive morbidity, and thus a shorter lifetime affected by cognitive impairment and dementia, in individuals aged 75 years and older living in Germany. METHODS: Our sample included n = 742 individuals of the population-based Leipzig Longitudinal Study of the Aged (LEILA75+; 1998-2013), who were free of dementia at baseline. The impact of higher education on compression of cognitive morbidity was studied by analyzing the association between education and (1) cognitive functioning over the study period and age at dementia onset, (2) age at death, and (3) the cumulative lifetime cognitive morbidity. RESULTS: Individuals with more years of education had a higher cumulative cognitive functioning over the lifetime period 75 to 100 years (weighted for survival probability), but not a later age of dementia onset nor a later age at death. CONCLUSION: Our results suggest, in individuals aged 75 years and older, higher education only compresses cognitive morbidity prior to dementia onset. Findings may be specific to countries where education is not a necessary requirement for access to good quality health care services.
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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 | 0.000 | 0.002 |
| 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".