Legacy Drug-Prescribing Patterns in Primary Care
Bibliographic record
Abstract
<h3>PURPOSE</h3> Polypharmacy is a key clinical challenge for primary care. Drugs that should be prescribed for an intermediate term (longer than 3 months, but not indefinitely) that are not appropriately discontinued could contribute to polypharmacy. We named this type of prescribing <i>legacy prescribing</i>. Commonly prescribed drugs with legacy prescribing potential include antidepressants, bisphosphonates, and proton pump inhibitors (PPIs). We evaluated the proportion of legacy prescribing within these drug classes. <h3>METHODS</h3> We conducted a population-based retrospective cohort study using prospectively collected data from the McMaster University Sentinel and Information Collaboration (MUSIC) Primary Care Practice Based Research Network, located in Hamilton, Ontario. All adult patients (aged 18 or older) in the MUSIC data set during 2010-2016 were included (N = 50,813). We calculated rates of legacy prescribing of antidepressants (prescription longer than 15 months), bisphosphonates (longer than 5.5 years), and PPIs (longer than 15 months). <h3>RESULTS</h3> The proportion of patients having a legacy prescription at some time during the study period was 46% (3,766 of 8,119) for antidepressants, 14% (228 of 1,592) for bisphosphonates, and 45% (2,885 of 6,414) for PPIs. Many of these patients held current prescriptions. The mean duration of prescribing for all legacy prescriptions was significantly longer than that for non–legacy prescriptions (<i>P</i> <.001). Concurrent legacy prescriptions for both antidepressants and PPIs was common, signaling a potential prescribing cascade. <h3>CONCLUSIONS</h3> The phenomenon of legacy prescribing appears prevalent. These data demonstrate the potential of legacy prescribing to contribute to unnecessary polypharmacy, providing an opportunity for system-level intervention in primary care with enormous potential benefit for patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".