Medication-related emergency department visits and hospitalizations among older adults.
Bibliographic record
Abstract
OBJECTIVE: To identify medications that have a high risk of adverse drug effects (ADEs) among seniors, using data from publicly available administrative databases. DESIGN: Cross-sectional study using the Discharge Abstracts Database (DAD) (which contains data on acute care institutions in all provinces and territories except Quebec), the National Ambulatory Care Reporting System (NACRS) (which contains data on emergency department [ED] visits in Ontario), and the IMS Brogan database Canadian CompuScript. SETTING: Canada. PARTICIPANTS: Adults 65 years of age and older with diagnostic codes for drugs, medicaments, and biologic substances causing adverse effects in therapeutic use. MAIN OUTCOME MEASURES: Adverse drug events from 2006 to 2008 associated with hospitalizations and ED visits among adults 65 years of age and older were identified by the DAD and the NACRS. The medications most frequently prescribed by primary care providers in 2008 were identified using data from Canadian CompuScript. RESULTS: From 2006 to 2008, the DAD identified 92 141 ADEs among older adults, and the NACRS identified 23 845 ADEs among older adults in Ontario EDs, which represented 2.9% of inpatients and 0.8% of ED patients (21.5% of whom were admitted to hospital). Drugs implicated in the DAD ADEs included anticoagulants (15.4%), antineoplastic agents (10.6%), opioids (9.2%), and nonsteroidal anti-inflammatory drugs (6.5%); drugs included in the ADEs of ED visits were anti-infective agents (15.9%), anticoagulants (14.2%), antineoplastic agents (9.6%), and opioids (7.3%). CONCLUSION: Among older adults, the drug classes most often associated with causing harm in the hospital setting and occurring out of proportion to the frequency prescribed were anticoagulants, opioids, antibiotics, and cardiovascular drugs. Thus, these drug classes should be the focus of quality improvement efforts in primary care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".