Accuracy of a Provincial Prescription Database for Assessing Medication Adherence in Heart Failure Patients
Bibliographic record
Abstract
BACKGROUND: British Columbia's central prescription database, PharmaNet, is often used for both clinical and research applications. However, PharmaNet details prescription transactions, not actual medication consumption, resulting in many potential sources of inaccuracy when the information is assumed to reflect population or individual drug utilization. OBJECTIVE: To assess the accuracy of PharmaNet for adherence assessment in patients with heart failure who are taking beta-blockers. METHODS: A 6-month prospective, longitudinal assessment of adherence to the prescribed beta-blocker regimen was carried out using both PharmaNet data and the Medication Event Monitoring System (MEMS) for each patient enrolled. The limit of agreement between the 2 adherence assessment methods was assessed using the Bland-Altman approach. RESULTS: Fifteen of 58 patients initially enrolled in the study were excluded, most due to misuse of MEMS or failure to return the MEMS vial despite thorough follow-up. For the 43 patients included in the final analysis, mean +/- SD adherence was 97.8 +/- 11.8% when assessed by PharmaNet and 97.1 +/- 7.3% when MEMS was used. However, the limit of agreement, reported as the mean of the differences +/- 2SD, was 6.8 +/- 18.5%, indicating a moderate-to-high level of agreement between the 2 methods when the confidence interval is taken into consideration. CONCLUSIONS: These results suggest that PharmaNet data accurately reflect medication adherence for most patients. The MEMS system proved unreliable in several cases, illustrating the difficulty of identifying a gold standard for adherence assessment.
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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 | 0.012 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".