Reducing pill burden and helping with medication awareness to improve adherence
Bibliographic record
Abstract
Medication nonadherence can be intentional when polypharmacy and high pill burden become overwhelming for patients. At the Bruyere Geriatric Day Hospital (GDH), patients referred for medication review take an average of 15 medications.1 The resulting complex regimens can lead to confusion about indications for medications, lack of certainty in their effectiveness and frustration. Patients increasingly believe that the multiple medications may not be needed and often elect to stop taking some or all of them. This case illustrates an approach to reducing the pill burden of polypharmacy that includes eliminating medications that are not working or are potentially harmful, reducing dosing frequency and using fixed combination products. Ultimately, identifying barriers to adherence and enhancing the patient’s understanding of the indication and proper use of medications, while reducing pill burden, assisted in improving adherence and disease control during a 12-week admission. A description of the GDH processes and in particular, communication about medication-related care, can be found in Appendix 1 (www.cpjournal.ca).
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".