Subjective memory concerns, poor vascular health, and male sex predict exacerbated memory decline trajectories: An integrative data-driven class and prediction analysis.
Bibliographic record
Abstract
OBJECTIVE: Subjective memory decline (SMD) has been identified as a potential early marker of nonnormal and accelerated cognitive decline. We performed data-driven analyses that integrated trajectory classification with prediction modeling to test declining trajectory class prediction by SMD facets, pulse pressure (PP; i.e., a robust proxy for vascular health), and sex. METHOD: = 70.2 years; 65% female) from the Victoria Longitudinal Study. First, latent class growth analyses identified distinct classes of memory trajectories. Second, we used the three-step method (R3STEP) to predict membership in the declining memory classes using six measures: memory complaints, memory concerns, memory compensation, memory self-efficacy, PP, and sex. RESULTS: First, we identified four classes of memory aging trajectories: (a) stable memory aging (STABLE), (b) typical memory aging (TYPICAL), (c) slowly declining memory aging (SLOW), and (d) rapidly declining memory aging (RAPID). Second, more memory concerns predicted membership in the SLOW and RAPID classes. Higher PP predicted membership in the SLOW class. Male sex predicted membership in the declining (TYPICAL, SLOW, RAPID) classes. CONCLUSION: Among SMD facets, memory concerns represent the most severe degree of apprehension about subjectively experienced memory losses. The present integrative data-driven analysis indicated that such concerns predicted membership in declining memory trajectory classes in addition to worse vascular health (higher PP) and sex (male). In nondemented aging, concerns about increasing memory failures may be veridical indicators of memory loss, especially when coupled with vascular comorbidity and being male. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".