The Impact of Memantine and Cholinesterase Inhibitor Initiation for Alzheimer Disease on the Use of Antipsychotic Agents: Analysis Using the Régie de l'Assurance Maladie du Québec Database
Bibliographic record
Abstract
OBJECTIVE: Patients with Alzheimer disease (AD) show a high incidence of behavioural and psychological symptoms of dementia, which often lead to the prescription of antipsychotics. Our study sought to assess the impact of the initiation of memantine or cholinesterase inhibitors (ChEIs) on the use of antipsychotics. METHOD: A retrospective cohort study was conducted using data from the Quebec provincial health plan database. Patients included in our study had received a diagnosis of AD and were initial users of memantine or ChEIs. The proportion of patients who used antipsychotics was estimated using prescription data dating back to 1 year before and to 1 year after the first prescription of memantine or ChEIs. The difference between the slopes corresponding to the periods pre- and postmemantine or ChEIs was analyzed using an interrupted time series design. RESULTS: The percentage of antipsychotic users increased by 118.3% before and by 68.3% after initiation of a ChEI, and increased by 68.6% before and by 7.0% after initiation of memantine. Antipsychotic trends pre- and post-ChEI initiation were not statistically different (P = 0.89), while a statistical difference was observed when comparing the antipsychotic trends pre- and postmemantine initiation (P < 0.001). CONCLUSIONS: The initiation of memantine, unlike ChEIs, has a notable stabilization effect on the prescription of antipsychotics in patients with 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".