Alzheimer's disease biomarkers as predictors of trajectories of depression and apathy in cognitively normal individuals, mild cognitive impairment<scp>,</scp> and Alzheimer's disease dementia
Bibliographic record
Abstract
Objectives To examine trajectories of depression and apathy over a 5‐year follow‐up period in (prodromal) Alzheimer's disease (AD), and to relate these trajectories to AD biomarkers. Methods The trajectories of depression and apathy (measured with the Neuropsychiatric Inventory or its questionnaire) were separately modeled using growth mixture models for two cohorts (National Alzheimer's Coordinating Center, NACC, n = 22 760 and Alzheimer's Disease Neuroimaging Initiative, ADNI, n = 1 733). The trajectories in ADNI were associated with baseline CSF AD biomarkers (Aβ42, t‐tau, and p‐tau) using bias‐corrected multinomial logistic regression. Results Multiple classes were identified, with the largest classes having no symptoms over time. Lower Aβ42 and higher tau (ie, more AD pathology) was associated with increased probability of depression and apathy over time, compared to classes without symptoms. Lower Aβ42 (but not tau) was associated with a steep increase of apathy, whereas higher tau (but not Aβ42) was associated with a steep decrease of apathy. Discussion The trajectories of depression and apathy in individuals on the AD spectrum are associated with AD biomarkers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".