Identifying potentially inappropriate prescribing in entire populations: coding the STOPP-START and Beers criteria for use with large, routinely collected population health datasets
Bibliographic record
Abstract
Background and Objective Adverse medication events significantly contribute to emergency department visits, unplanned hospitalizations, and in-hospital morbidity and mortality, particularly in older adults. Identifying potentially inappropriate prescribing is essential to improve safety, care quality, and medication management. The objective of this study was to identify and codify the STOPP-START and Beers criteria for large, population-level health databases to detect potentially inappropriate prescribing at the population level. Methods A subset of the 2014 STOPP/START and 2015 Beers criteria applicable to health administrative data were codified using diagnostic (ICD; International Classification of Diseases) and medication (DIN; Drug Identification Number) codes using provincial health administrative databases in Ontario, Canada, which comprise individual-level, linked information on medication dispensation, physician services use, emergency room visits, hospitalizations, mortality, and sociodemographic data. Results Overall, 103 of 177 (58.2%) criteria were codable (76.5% of 81 STOPP, 23.5% of 34 START, and 53.2% of 62 Beers). Some criteria could not be coded because the population health data used was missing information necessary to the operationalization these criteria. This included laboratory values (renal function) and diagnostic or clinical information (such as blood pressure, body mass index) that could not be indirectly derived from the population health data. Conclusion Applying a large subset of codified and well-established clinical criteria to health administrative data offers a promising and potentially cost-effective approach to detect potentially inappropriate prescribing at the population level. The present study contributes the required coding which constitutes the core of this approach.
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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 | 0.017 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".