Persistence with cholinesterase inhibitor therapy in a population‐based cohort of patients with Alzheimer's disease
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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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".