A Descriptive Quantitative Analysis on the Extent of Polypharmacy in Recipients of Ontario Primary Care Team Pharmacist-Led Medication Reviews
Bibliographic record
Abstract
Pharmacist-led medication reviews have been shown to improve medication management, reducing the adverse effects of polypharmacy among older adults. This paper quantitatively examines the medications, medication discrepancies and drug therapy problems of recipients in primary care. A convenience sample of 16 primary care team pharmacists in Ontario, Canada contributed data for patients with whom they conducted a medication review over a prior four-week period. Data were uploaded using electronic data capture forms and descriptive analyses were completed. Two hundred and thirty-seven patients (on average, 67.9 years old) were included in the study, taking an average of 9.2 prescription medications ( ± 4.7). Majority of these patients (83.5%) were categorized as polypharmacy patients taking at least five or more prescribed drugs per day. Just over half of the patients were classified as having a low level of medication complexity (52.3%). Pharmacists identified 2.1 medication discrepancies ( ± 3.9) and 3.6 drug therapy problems per patient ( ± 2.8). Half these patients had more than one medication discrepancy and almost every patient had a drug therapy problem identified. Medication reviews conducted by pharmacists in primary care teams minimized medication discrepancies and addressed drug therapy problems to improve medication management and reduce adverse events that may result from polypharmacy.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".