Linking Patients’ Goals and Priorities to Recommendations for Medication Changes in a Polypharmacy-Focused Structured Clinical Pathway
Bibliographic record
Abstract
Polypharmacy is associated with poorer health outcomes in older adults. It is challenging to minimize the harmful effects of medications while maximizing benefits of single-disease-focused recommendations. Integrating patient input can balance these factors. The objectives are to describe the goals, priorities, and preferences of participants asked about these in a structured process to polypharmacy, and to describe the extent that decision-making within the process mapped onto these, signaling a patient-centered approach. This is a single-group quasi-experimental study, nested within a feasibility randomized controlled trial. Patient goals and priorities were mapped to medication recommendations made during the intervention. Overall, there were 33 participants who reported 55 functional goals and 66 symptom priorities, and 16 participants reported unwanted medications. Overall, 154 recommendations for medication alterations occurred. Of those, 68 (44%) recommendations mapped to the individual's goals and priorities, whereas the rest were based on clinical judgment where no priorities were expressed. Our results signal this process supports a patient-centered approach: allowing conversations around goals and priorities in a structured process to polypharmacy should be integrated into subsequent medication decisions.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".