Polypharmacy and use of potentially inappropriate medications in long-term care facilities: does coordinated primary care make a difference?
Bibliographic record
Abstract
OBJECTIVES: Polypharmacy is both common and harmful for frail residents of long-term care facilities (LTCF). We aimed to study rates of polypharmacy and potentially inappropriate medications (PIMs) before and after the implementation of a new model of coordinated primary care in LTCF, 'Care by Design (CBD)'. METHODS: This was an observational before/after study in 10 LTCFs in Halifax, NS, Canada. Chart reviews were conducted for 529 LTCF residents for whom medication use was available. Both regularly scheduled and PRN medications were included but topical, inhaled and other non-systemic agents were excluded. Polypharmacy was defined as the concomitant use of more than 10 medications. PIMs were identified using Beers Criteria. KEY FINDINGS: Mean age of LTCF residents was older pre- versus post-CBD (85.7 versus 82.1 years; P = 0.0015). The burden of polypharmacy was high, but decreased significantly from 86.8% pre-CBD to 79.5% post-CBD (P = 0.046). The mean number of medications per resident decreased from 16.7 (SD 5.6) pre- to 15.5 (SD 6.2) post-CBD (P = 0.037). Residents with dementia were taking fewer medications both overall and following the implementation of CBD (mean 15.9, SD 0.6 pre-CBD versus 14.4, SD 0.4 post-CBD; P = 0.04). PIM rates were high and showed no change with CBD (86.2% versus 81.1%, P = 0.16). CONCLUSIONS: Polypharmacy was the norm of this sample of LTCF residents. Implementation of coordinated care through the CBD model was associated with a small decrease in polypharmacy but not overall use of PIMs. Further targeted efforts are required to substantially reduce both polypharmacy and PIMs in clinical practice.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".