Education as a Moderator of the Relationship Between Episodic Memory and Amyloid Load in Normal Aging
Bibliographic record
Abstract
The current study explored whether education, a proxy of cognitive reserve, modifies the association between episodic memory (EM) performance and βeta-amyloid load (Aβ), a biomarker of Alzheimer's disease, in a cohort of cognitively normal older adults. One hundred and four participants (mean age 73.3 years) evenly spread out in three bands of education were recruited. Participants underwent neuropsychological assessment, structural MRI as well as PET imaging to quantify Aβ load. Moderation analyses and the Johnson-Neyman technique were carried out to examine the interaction of education with Aβ load to predict EM performance. Linear regressions were then performed within each group of education to better illustrate the interaction effect (all analyses were controlled for age and sex). The interaction between education and Aβ load was significant (p < .05) for years of education, reaching a cutoff point of 13.5 years, above which the relationship between Aβ load and EM was no longer significant. Similarly, significant associations were found between Aβ and EM among participants with secondary (p < .01) and pre-university education (p < .01), but not with a university degree (p = .253). EM performance is associated with Aβ load in cognitively normal older individuals, and this relationship is moderated by educational attainment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".