Validation of drug prescription records for senior patients in Alberta's Tomorrow Project: Assessing agreement between two population‐level administrative pharmaceutical databases in Alberta, Canada
Why this work is in the frame
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Bibliographic record
Abstract
PURPOSE: To assess agreement between the Pharmaceutical Information Network (PIN), a newly implemented medication data repository in Alberta, Canada, and the Alberta Blue Cross (ABC) database, a long established database with medication records of all senior patients in Alberta. METHODS: PIN data (2008-2015) were cross-validated with ABC medication records for senior participants (older than 65 years old) in Alberta's Tomorrow Project (ATP), a longitudinal cohort study in Alberta. The completeness and accuracy of PIN were respectively calculated as the percentage of ABC records coexisting (concordant) in PIN and the percentage of concordant records having mutually agreeable information on drug quantity. Generalized linear models were used to examine potential association of PIN completeness and accuracy with sociodemographic factors. RESULTS: A total of 1 218 191 drug prescription records from 13 143 ATP participants were captured by PIN and ABC in 2008-2015, among which 91.6% were from PIN, 82.5% from ABC, and 74.2% coexisted in PIN and ABC. The overall completeness of PIN in capturing ABC medication records was 89.9%, with small variations (less than ±5%) across types of drugs. The completeness of PIN was improved on average by 1.3% annually over time (P < .001). PIN had 100% accuracy as defined by drug quantity data agreeable with ABC records. No significant associations were observed with age, sex, ethnicity, rural/urban areas, and socioeconomic status of the participants. CONCLUSIONS: Cross-validated with the ABC dataset, our study showed that irrespective of drug type, PIN has a fairly good completeness (approximately 90%) and accuracy (100%) in capturing the ABC claimed medications for senior patients in Alberta.
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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 | 0.009 | 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 it