P5601Polypharmacy: A reality in ST-elevation myocardial infarction
Bibliographic record
Abstract
Background: Increasing dispensing rates of multiple medications, especially among the elderly, has mandated informed evaluation of the risks and benefits associated with polypharmacy. We examined the rates and types of medication use among patients discharged from hospital after ST-elevation myocardial infarction (STEMI) in the universal single-payer/single provider healthcare system in Canada. Methods: A population-level integrated database linking hospitalizations, outpatient visits, and pharmaceutical claims data was used to identify all patients >18 years old who were discharged alive from hospital with a primary diagnosis of STEMI and survived to 6-months. Pharmaceutical claims during these 6-months were used to examine the number of prescriptions, by class of drug, and by anatomical therapeutic chemical (ATC) code (which indicates the organ or system on which they act). Results: The cohort consisted of 7741 patients with a median (M) age of 60 years (inter-quartile range (IQR): 52 - 70). The proportion of elderly (age ≥65 years) and females was 37% and 24%, respectively; 63% had a Charlson comorbidity score of 0 (low risk), 27% had a score of 1–2, and 10% had a score of 3 or more (high risk). On average, STEMI patients were on a combination of 8 drugs (Figure 1), and 25% of the patients were prescribed ≥10 drugs. The number of drugs dispensed were higher among the elderly (M 9, IQR: 7–12) compared to younger patients (M 7, IQR: 5–10, p<0.01); and among women (M 9, IQR: 7–12) compared to men (M 7, IQR: 5–10, p<0.01). When prescriptions were grouped by ATC, 7173 (93%) of patients were dispensed drugs related to the cardiovascular system. The CIRCOS© plot (Figure 2) shows the proportion of the 7173 patients on other ATC drugs, the most common of which were related to the gastro-intestinal tract and metabolism (53%), the nervous system (47%), or were anti-infectives (27%).
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".