Measuring Multimorbidity, Classifying Potentially Inappropriate Medications and Investigating Polypharmacy in Recipients of Medication Reviews and Pharmacy Dispensing Services Conducted by Ontario Community Pharmacists
Bibliographic record
Abstract
Factors including aging and health related behaviours lead to multimorbidity, polypharmacy and potentially inappropriate medications (PIMs). Medication reviews conducted by community pharmacists can improve medication management. Patient data were extracted from three community pharmacies in Ontario. Multimorbidity scores and number of PIMs were determined. Construct validity was assessed via linear regressions with age and sex. Consistency among multimorbidity measures was assessed via Pearson correlation coefficients. The mean multimorbidity scores determined by disease counts, Charlson Index, Chronic Disease Score, Medication Based Disease Burden Index, and Rx-Risk measures were 4.9 (SD 2.3), 4.4 (SD 2.0), 7.1 (SD 3.6), 0.1 (SD 0.2), and 0.4 (SD 1.7) respectively. Most analyses supported construct validity and consistency among measures. Patients were taking a mean of 1.9 (SD 1.5) and 2.0 (SD 1.4) PIMs determined by the 2019 American Geriatrics Society Beers criteria and STOPP criteria respectively. The analyses can lead to the improved management of multimorbidity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".