Pharmacosurveillance without borders: electronic health records in different countries can be used to address important methodological issues in estimating the risk of adverse events
Bibliographic record
Abstract
OBJECTIVES: Evaluate methodological advantages and limitations of an international pharmacosurveillance system based on electronic health records (EHRs). STUDY DESIGN AND SETTINGS: Type 2 diabetes was used as an exemplar. Cohorts of newly treated diabetics were followed in each country (Quebec, Canada; Massachusetts, United States; Manchester, UK) from 2009 to 2012 using local EHR systems. Cox proportional hazards models were used to assess the risk of cardiovascular events. RESULTS: A total of 44,913 newly treated diabetics were identified; 82.6% (United States) to 93.1% (Canada) were started on biguanides; 13% of patients failed to fill initial prescriptions. An increased risk of cardiovascular events with sulfonylureas was observed when dispensing [hazard ratio (HR): 2.83] vs. EHR prescribing (HR: 2.47) data were used. The addition of clinical data produced a threefold to 10-fold increase in comorbidity for obesity and renal disease, but had no impact on the risk of different hypoglycemic therapies. The risk of cardiovascular events with sulfonylureas was higher in the United States [HR: 3.4; 95% confidence interval (CI): 2.1, 5.5] compared to England (HR: 1.3; 95% CI: 1.1, 1.6). CONCLUSION: An international surveillance system based on EHRs may provide more timely information about drug safety and new opportunities to estimate potential sources of bias and health system effects on drug-related outcomes.
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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 | 0.129 | 0.413 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".