DESCRIBING THE EVOLUTION OF MEDICATION USE OVER TIME IN PEOPLE LIVING WITH DEMENTIA USING NETWORK ANALYSIS
Bibliographic record
Abstract
Abstract Prescribing for community-dwelling older adults living with dementia is complex. Multiple medications may be used to manage symptoms associated with dementia and/or co-existing chronic conditions, and can lead to problematic polypharmacy. Our objective was to use network analysis, a data science method, to provide a comprehensive description of co-prescribed medications in persons with dementia and describe whether these patterns change over time. We created a population-based cohort of community-dwelling older adults (aged 67+ years) in Ontario, Canada, newly diagnosed with dementia (between April 2014 and January 2019), from health administrative data, and developed medication networks at one year prior to, at, and for up to five years following dementia diagnosis. Among 136,292 individuals newly diagnosed with dementia, the mean age was 82.2 years and 59% were female. The most common medication subclasses dispensed at diagnosis were primarily cardiovascular medications: statins (45.6%), proton pump inhibitors (27.3%), beta-blockers (27.0%), calcium blockers (25.1%), and ACE inhibitors (24.6%). Similar proportions of medication subclasses were found at five years after diagnosis, except cholinesterase inhibitors (34.0% at five years were dispensed cholinesterase inhibitors compared to 16.9% at diagnosis). The most frequent co-prescribed medication pairs at diagnosis included statins and beta-blockers (16.0%), proton pump inhibitors (16.0%), and ace inhibitors (15.4%), respectively. Co-prescription was similar at five years, but also included higher frequency of co-prescribing with cholinesterase inhibitors (e.g., 19.4% were prescribed cholinesterase inhibitors and statins). Network diagrams demonstrate the complexity of prescribing in this population and highlight concurrent prescribing which may require careful monitoring or deprescribing.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".