Quality of education impacts late‐life cognition
Bibliographic record
Abstract
OBJECTIVES: Screening tests of global cognition detect racial differences in scores even after adjustment for educational attainment. Differential educational environments in adolescence may affect individual cognitive function. This study examines the impact of high school educational quality on late-life cognition among community-dwelling older adults. METHODS/DESIGN: Data were collected from community-dwelling individuals from the Philadelphia Healthy Brain Aging (PHBA) cohort at the University of Pennsylvania Health System. The present analysis included subjects from the PHBA over the age of 55 years without a diagnosis of Parkinson's disease or dementia, who had attended high school in the City of Philadelphia. Cognition was assessed using the Montreal Cognitive Assessment (MoCA); clinical information was abstracted from the subject's electronic health record. High school information was obtained from the Philadelphia Board of Education. After univariable correlations were defined, we performed stepwise multiple linear regression models to determine the most significant predictors of late-life cognitive status. RESULTS: A total of 130 subjects meeting inclusion criteria were included in the analysis. Years of education, race, educational level, school district, and financial status were all positively associated with MoCA. Significant negative associations included composite vascular risk, attendance at highly segregated schools, and historical poverty status. In stepwise multiple linear regression modeling, the impact of race on cognition remained significant when educational attainment was added to the model but was no longer significant once segregation status was added. CONCLUSIONS: This work suggests that academic and community factors beyond years of education have a marked impact on late-life cognition.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".