Biological embedding of educational disparities in aging and neurodegeneration across global settings
Bibliographic record
Abstract
BACKGROUND: While education is crucial for brain health, evidence mainly relies on individual measures of years of education (YoE), neglecting educational quality (EQ). Whether YoE and EQ have complementary impacts on aging and dementia is unknown. METHODS: We assessed the impact of EQ and YoE on brain health in 7,533 subjects from 20 countries, including healthy controls (HCs), Alzheimer's disease (AD), and frontotemporal lobar degeneration (FTLD). EQ was based on country-level quality indicators. After applying neuroimage harmonization, we examined their effect on gray matter volume and functional connectivity. Regression models were adjusted for age, sex, and cognition, controlling for multiple comparisons. The impact of image quality was controlled through sensitivity analysis. RESULTS: Less EQ and YoE were associated with greater brain burden across groups. However, EQ had a stronger impact, mainly targeting the vulnerable areas of each condition. At the whole-brain level, EQ influenced atrophy (HCs: ∆mean = 2.0 [1.9-2.0] CL95 × 10⁻², p < 10⁻⁵; AD: ∆mean = 0.1 [-0.0-0.3] CL95 × 10⁻², p = 0.18; FTLD: ∆mean = 3.5 [3.0-4.0] CL95 × 10⁻², p < 10⁻⁵) and networks (HCs: ∆mean = 13.5 [13.2-13.7] CL95 × 10⁻², p < 10⁻⁵; AD: ∆mean = 5.9 [5.2-6.7] CL95 × 10⁻², p < 10⁻⁵; FTLD: ∆mean = 13.2 [11.2-13.7] CL95 × 10⁻², p < 10⁻⁵), 1.3 to 7.0 times more than YoE. CONCLUSION: Results support the need to incorporate education quality to study and improve brain health, underscoring the importance of country-level measures.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".