Defining the duration of the dispensation of oral anticoagulants in administrative healthcare databases
Bibliographic record
Abstract
PURPOSE: In clinical practice, warfarin therapy requires frequent dose adjustments. In pharmacy claims, the days supplied value may not reflect the true duration of warfarin dispensation. This may affect the measures of association comparing the safety of direct oral anticoagulants (DOACs) versus warfarin. METHODS: Using Quebec healthcare administrative databases, we formed a cohort of 55 230 patients newly treated with oral anticoagulants between 2010 and 2016. The duration of dispensations was defined using two approaches: the recorded days supplied value, and the longitudinal coverage approximation (data-driven) that may account for individual variation in drug usage patterns. Propensity scores adjusted Cox proportional hazards regression models were used to estimate the hazard ratio (HR) of major bleeding with dabigatran or rivaroxaban versus warfarin. RESULTS: Using the days supplied, the mean (and standard deviation) dispensation durations for dabigatran, rivaroxaban, and warfarin were 19 (15), 19 (14), and 13 (12) days, respectively. Using the data-driven approach, the durations were 20 (16), 19 (15), and 15 (16) days, respectively. The choice of the approach had no impact on the HR estimates. CONCLUSIONS: In our settings, the data-driven approach closely approximated the recorded days supplied value for the standard dose therapies such as dabigatran and rivaroxaban. For warfarin, the data-driven approach captured more variability in the duration of dispensations compared to the days supplied value, which may better reflect the true drug-taking behavior of warfarin. Both approaches may provide valid estimates when comparing the safety of DOACs versus warfarin.
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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 | 0.014 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".