Methodological challenges in assessment of current use of warfarin among patients with atrial fibrillation using dispensation data from administrative health care databases
Bibliographic record
Abstract
PURPOSE: Algorithms to define current exposure to warfarin using administrative data may be imprecise. Study objectives were to characterize dispensation patterns, to measure gaps between expected and observed refill dates for warfarin and direct oral anticoagulants (DOACs). METHODS: Retrospective cohort study using administrative health care databases of the Régie de l'assurance-maladie du Québec. We identified every dispensation of warfarin, dabigatran, rivaroxaban, or apixaban for patients with AF initiating oral anticoagulants between 2010 and 2015. For each dispensation, we extracted date and duration. Refill gaps were calculated as difference between expected and observed dates of successive dispensation. Refill gaps were summarized using descriptive statistics. To account for repeated observations nested within patients and to assess the components of variance of refill gaps, we used unconditional multilevel linear models. RESULTS: We identified 61 516 new users. Majority were prescribed warfarin (60.3%), followed by rivaroxaban (16.4%), dabigatran (14.5%), apixaban (8.8%). Most frequent recorded duration of dispensation was 7 days, suggesting use of pharmacist-prepared weekly pillboxes. The average refill gap from multilevel model was higher for warfarin (9.28 days, 95%CI:8.97-9.59) compared with DOACs (apixaban 3.08 days, 95%CI: 2.96-3.20, dabigatran 3.70, 95%CI: 3.56-3.84, rivaroxaban 3.15, 95%CI: 3.03-3.27). The variance of refill gaps was greater among warfarin users than among DOAC users. CONCLUSIONS: Greater refill gaps for warfarin may reflect inadequate capture of the period covered by the number of dispensed pills recorded in administrative data. A time-dependent definition of exposure using dispensation data would lead to greater misclassification of warfarin than DOACs use.
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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 | 0.292 | 0.600 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".