Assessing agreement between population-level administrative pharmaceutical databases and patient-reported medication dispensation in cardiac rehabilitation patients
Bibliographic record
Abstract
BACKGROUND: Pharmacoepidemiology has emerged as a crucial field in evaluating the use and effects of medications in large populations to ensure their safe and effective use. This study aimed to assess the agreement of cardiac medication use between a provincial medication database, the Pharmaceutical Information Network (PIN), and reconciled medication data from confirmation through patient interviews for patients referred to cardiac rehabilitation. METHODS: The study included data from patients referred to the TotalCardiology Rehabilitation CR program, and medication data was available in both TotalCardiology Rehabilitation charts and PIN. The accuracy of medication data obtained from patient interviews was compared to that obtained from PIN with proportions and kappa statistics to evaluate the reliability of PIN data in assessing medication use. RESULTS: Patient-reported usage was higher for statins (41.6 %) vs. 38.4 %), ACE/ARB, beta-blockers (75.7 %) vs. 73.7 %), DOAC (3.5 %) vs. 2.6 %), and ADP-receptor antagonists (71.0 %) vs. 68.1 %) than if PIN was used. Patient-reported usage data was lower for Ezetimibe (4.7 vs. 4.8 %), Aldosterone antagonists (5.4 %) vs. 5.5 %), digoxin (0.9 %) vs. 1.0 %), calcium channel blockers (19.2 vs. 19.9 %) and warfarin (7.2 %) vs. 8.1 %). The results indicated that the differences between the two sources were very small, with an average agreement of 95.3 % and a kappa of 0.70. CONCLUSION: The study's results, which show a high level of agreement between PIN and patient self-reporting, affirm the reliability of PIN data as a source for obtaining an accurate assessment of medication use. This finding is crucial in the context of pharmacoepidemiology research, where the accuracy of data is paramount. Further research to explore the complementary use of both data sources will be valuable.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".