Biopsychosocial Characterization of Cognitive Decline and Late-Life Depression Trajectories Using Bayesian Consensus Clustering and Machine Learning
Bibliographic record
Abstract
Cognitive decline and late-life depression (LLD) often co-occur and impact elderly’s wellbeing. However, their intersection and antecedents are understudied. We analyzed 2,992 participants from the Religious Orders Study and Memory and Aging Project, integrating symptoms and diagnostic records with Bayesian consensus clustering to define latent sub-trajectories of cognitive decline and LLD. Logistic regression, elastic-net regression and XGBoost were used to build models of these trajectories including n=57 biopsychosocial predictors. Associations of subgroups with postmortem neuropathologies were assessed for 1,721 deceased participants. Three subgroups were identified for cognitive decline and two for LLD, which overlapped significantly (chi-square p=8.1x10-26). Elastic-net regression performed best overall, while XGBoost excelled at predicting moderate subgroups. High neuroticism and low physical health at baseline predicted unhealthy subgroups for both cognition and LLD. There were no neuropathologies associated with LLD trajectories. Our results suggest potential values of targeting neuroticism and physical health for healthy aging and patient screening.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".