Anticholinergic Drug Burden in Persons with Dementia Taking a Cholinesterase Inhibitor: The Effect of Multiple Physicians
Bibliographic record
Abstract
OBJECTIVES: To explore the association between the number of physicians providing care and anticholinergic drug burden in older persons newly initiated on cholinesterase inhibitor therapy for the management of dementia. DESIGN: Population-based cross-sectional study. SETTING: Community and long-term care, Ontario, Canada. PARTICIPANTS: Community-dwelling (n = 79,067, mean age 81.0, 60.8% female) and long-term care residing (n = 12,113, mean age 84.3, 67.2% female) older adults (≥66) newly dispensed cholinesterase inhibitor drug therapy. MEASUREMENTS: Anticholinergic drug burden in the prior year measured using the Anticholinergic Risk Scale. RESULTS: Community-dwelling participants had seen an average of eight different physicians in the prior year. The odds of high anticholinergic drug burden (Anticholinergic Risk Scale score ≥ 2) were 24% higher for every five additional physicians providing care to individuals in the prior year (adjusted odds ratio = 1.24, 95% confidence interval = 1.21-1.26). Female sex, low-income status, previous hospitalization, and higher comorbidity score were also associated with high anticholinergic drug burden. Long-term care facility residents had seen an average of 10 different physicians in the prior year. After a sensitivity analysis, the association between high anticholinergic burden and number of physicians was no longer statistically significant in the long-term care group. CONCLUSION: In older adults newly started on cholinesterase inhibitor drug therapy, greater number of physicians providing care was associated with higher anticholinergic drug burden scores. Given the potential risks of anticholinergic drug use, improved communication among physicians and an anticholinergic medication review before prescribing a new drug are important strategies to improve prescribing quality.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".