Alzheimer’s Disease Stage Transitions Among United States Veterans
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) and related dementias are progressive neurological disorders with stage-specific clinical features and challenges. An important knowledge gap is the "window of time" within which patients transition from mild cognitive impairment or mild AD to moderate or severe AD. Better characterization/establishment of transition times would help clinicians initiating treatments, including anti-amyloid therapy. OBJECTIVE: To describe cognitive test score-based AD stage transitions in Veterans with AD in the US Veterans Affairs Healthcare System (VAHS). METHODS: This retrospective analysis (2010-2019) identified Veterans with AD from the VAHS Electronic Health Record (EHR) notes. AD stage was based on Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), or Saint Louis University Mental Status (SLUMS) Examination scores in the EHR. RESULTS: We identified 296,519 Veterans with cognitive test-based AD staging. Over the 10-year study, the proportion of veterans with MMSE scores declined from 24.9% to 9.5% while those with SLUMS rose from 9.0% to 17.8%; and MoCA rose from 5.0% to 25.4%. The average forward transition times between each stage were approximately 2-4 years, whether assessed by MMSE, MoCA, or SLUMS. CONCLUSION: The average transition time for cognitive test-based assessments of initial cognitive decline, early-stage AD, and moderate/severe AD in the VAHS is 2-4 years. In view of the short window for introducing disease-modifying therapy and the significant benefits of early treatment of AD, our data suggest a critical need for treatment guidelines in the management of AD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".