Persistence with cholinesterase inhibitor therapy in a population‐based cohort of patients with Alzheimer's disease
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: To estimate the risk (and determinants) of discontinuing cholinesterase inhibitors (ChEIs) in a population-based sample of Alzheimer's disease (AD) patients. METHODS: This is a retrospective cohort study based on linked de-identified administrative health data from the province of Saskatchewan, Canada. The cohort included all AD patients receiving a ChEI prescription during the first year of provincial coverage (2000-2001). Persistence was defined as no gap of 60+ days between depletion and subsequent refill of a ChEI prescription. Kaplan-Meier analysis was used to estimate the risk of discontinuation over 40 months. Cox regression with time-varying covariates was used to assess risk factors for ChEI discontinuation. RESULTS: The sample included 1080 patients (64% female, average age 80 +/- 7 years). Baseline mean (SD) Mini-Mental State Examination (MMSE) and Functional Activities Questionnaire (FAQ) scores were 20.8 (4.4) and 17.5 (7.7), respectively. Over 40 months, 84% discontinued therapy. The 1-year risk of discontinuation was 66.4% (95%CI 63.5-69.3%). Discontinuation was significantly more likely for females (adjusted HR 1.34, 95%CI 1.16-1.55) and among those with lower MMSE scores (2.52, 2.01-3.17 if <15), not receiving social assistance (1.25, 1.07-1.45), and paying at least 65% of total prescription costs (1.51, 1.30-1.74). It was significantly less likely for patients with frequent physician visits (0.78, 0.66-0.93, for 7-19 vs. <7 visits), higher Chronic Disease Scores (0.74, 0.61-0.89, for 7+ vs. <4), and FAQ scores of 9+ (0.82, 0.69-0.99). CONCLUSION: The likelihood of discontinuing ChEI therapy was high in this real-world sample of AD patients. Significant predictors included clinical, socioeconomic, and practice factors.
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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 | 0.001 | 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 it