Assessing potentially inappropriate prescribing (PIP) and predicting patient outcomes in Ontario's older population: a population-based cohort study applying subsets of the STOPP/START and Beers' criteria in large health administrative databases.
Bibliographic record
Abstract
Introduction Adverse drug events (ADEs) are common in older people and contribute significantly to emergency department (ED) visits, unplanned hospitalisations, healthcare costs, morbidity and mortality. Many ADEs are avoidable if attention is directed towards identifying and preventing inappropriate drug use and undesirable drug combinations. Tools exist to identify potentially inappropriate prescribing (PIP) in clinical settings, but they are underused. Applying PIP assessment tools to population-wide health administrative data could provide an opportunity to assess the impact of PIP on individual patients as well as on the healthcare system. This would open new possibilities for interventions to monitor and optimise medication management on a broader, population-level scale.\n\nMethods and analysis The aim of this study is to describe the occurrence of PIP in Ontario's older population (aged 65 years and older), and to assess the health outcomes and health system costs associated with PIP—more specifically, the association between PIP and the occurrence of ED visits, hospitalisations and death, and their related costs. This will be done within the framework of a population-based retrospective cohort study using Ontario's large health administrative and population databases. Eligible patients aged 66 years and older who were issued at least 1 prescription between 1 April 2003 and 31 March 2014 (approximately 2 million patients) will be included.\n\nEthics and dissemination Ethical approval was obtained from the Ottawa Health Services Network Ethical Review Board and from the Bruyère Research Institute Ethics Review Board. Dissemination will occur via publication, presentation at national and international conferences, and ongoing exchanges with regional, provincial and national stakeholders, including the Ontario Drug Policy Research Network and the Ontario Ministry of Health and Long-Term Care.\n\nTrial registration number Registered with clinicaltrials.gov (registration number: NCT02555891).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".