Identification of inappropriate medication use in elderly patients with frequent emergency department visits
Bibliographic record
Abstract
OBJECTIVE: To determine the demographic and health care characteristics of elderly family health team patients who are frequent emergency department (ED) users, focusing on potentially inappropriate medications (PIMs) and access to primary care services. DESIGN: Cross-sectional retrospective chart review. SETTING: Academic family medicine clinic in Toronto, Ontario. PARTICIPANTS: A total of 46 elderly patients (age >65 years) with 4 or more visits to a University Health Network-affiliated ED between April 1, 2010, and March 31, 2011. MAIN OUTCOME MEASURES: Using the validated STOPP (Screening Tool of Older Persons' potentially inappropriate Prescriptions) criteria, PIMs were identified. The primary objective was to determine whether PIMs were associated with more frequent ED use. The secondary objective was to determine whether patients who had previously undergone a clinic pharmacist-led medication review had fewer PIMs. We also determined the health characteristics of these patients at the time of their last ED visit of the study period. Utilization of primary care resources, both prior to and after ED visits, was determined. RESULTS: Sixty-five percent of patients were taking at least 1 PIM. The total number of PIMs in the study population was 71. Having more PIMs was significantly correlated with a higher number of ED visits (r = 0.32, p < 0.05). Patients with a previous medication review had a similar number of PIMs compared with those without a review. The mean number of concurrent medications was 12.1 and the mean Charlson Comorbidity Index score was 3.7. Significant delay between hospital discharge and primary care follow-up (median 13 days) was observed. CONCLUSION: Elderly patients who are more frequent ED users have a greater number of PIMs. Primary care resources appear to be underused in this population.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".