The Role of Mental Health Conditions in Early Detection and Treatment of Veterans With Alzheimer’s Dementia
Bibliographic record
Abstract
INTRODUCTION: The benefits of early detection of Alzheimer's disease (AD) have become increasingly recognized. Veterans with mental health conditions (MHCs) may be less likely to receive a specific AD diagnosis compared to veterans without MHCs. We investigated whether rates of MHCs differed between veterans diagnosed with unspecified dementia (UD) vs. AD to better understand the role MHCs might play in establishing a diagnosis of AD. MATERIALS AND METHODS: This retrospective analysis (2015-2022) identified UD and AD with diagnostic code-based criteria. We determined the proportion of veterans with MHCs in UD vs. AD cohorts. Secondarily, we assessed the distribution of UD/AD diagnoses in veterans with and without MHCs. RESULTS: We identified 145,309 veterans with UD and 33,996 with AD. The proportion of each MHC was consistently higher in UD vs. AD cohorts: 41.4% vs. 33.2% (depression), 26.9% vs. 20.3% (post-traumatic stress disorder), 23.4% vs. 18.2% (anxiety), 4.3% vs. 2.1% (bipolar disorder), and 3.9% vs. 1.5% (schizophrenia). The UD diagnostic code was used in 84% of veterans with MHCs vs. 78% without MHCs (P < .001). CONCLUSIONS: Mental health conditions were more likely in veterans with UD vs. AD diagnoses; comorbid MHC may contribute to delayed AD diagnosis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".