Diagnosis and Management of Alzheimer’s Disease in Primary Care: A Real-World Study in Ontario, Canada
Bibliographic record
Abstract
OBJECTIVE: To understand the real-world clinical practice patterns and variation in Alzheimer's disease (AD) diagnostic and screening tool utilization by primary care physicians (PCPs), including tools used for assessing dementia/AD severity and subsequent treatment patterns. METHODS: This retrospective observational study used de-identified primary care data from electronic medical records (EMR) data provided by the researchers from Queen's University, Ontario, Canada from August 2011 to August 2021. Individuals ≥50 years old with dementia or AD were identified using AD and dementia-related diagnostic codes, medications, and keywords searched using natural language processing (NLP) and Artificial Intelligence (AI) algorithms from EMR chart notes. Diagnostic and screening tools included scales, neuroimaging, and laboratory tests. Medications examined were cholinesterase inhibitors, memantine, antidepressants, and antipsychotics. RESULTS: The study cohort included 417 individuals with all-cause dementia (mean [standard deviation: SD] age: 78.86 [0.19] years), and 71 individuals with AD (mean [SD] age: 76.13 [1.07]). The most-used scale was the Montreal Cognitive Assessment (MoCA; dementia: 53.2%, AD: 84.5%). The mean [SD] frequency of MoCA administration doubled in the year following AD index date compared to the year prior (0.29 [0.82] to 0.67 [1.19] times per patient-year). Severity scores, often unspecified, suggested various stages of cognitive impairment. Among the medications examined, cholinesterase inhibitors were prescribed in 27.8% (n = 116) and 57.8% (n = 41) of people with dementia and AD, respectively. Antidepressants were the most frequently prescribed medication examined (dementia: 49.6%; AD: 71.8%). CONCLUSION: PCPs play an important role in the early detection and management of dementia/AD. As new biomarkers and therapies emerge for early AD, there is a need for connected health system data to guide PCPs through the early diagnostic process.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".