Are Brand-Name and Generic Warfarin Interchangeable? Multiple N-of-1 Randomized, Crossover Trials
Bibliographic record
Abstract
BACKGROUND: Warfarin is a commonly used anticoagulant in North America. Several generic formulations have been approved, raising concern over the safety and efficacy of these products compared with brand-name Coumadin. OBJECTIVE: To ensure that generic warfarin products can be safely interchanged with Coumadin. METHODS: Multiple n-of-1 randomized, double-blind, crossover trials switched outpatients (N = 7) between a generic warfarin formulation (Apo-warfarin) and Coumadin over 30 weeks. Study patients took each drug for five 3-week periods, with international normalized ratio (INR) measurements taken twice per period. Inter- and intrapatient differences between generic warfarin and Coumadin were compared, and overall study patient results were compared with those of a Coumadin control group. RESULTS: There were no differences between warfarin products in terms of mean INR results or number of dosage adjustments required. There also was no difference in INR variation based on warfarin formulation (p > 0.69), nor was a patient and warfarin interaction found (p > 0.81). The INR results were not influenced by whether patients were maintained on Coumadin only (control group) or interchanged between Coumadin and generic warfarin (p = 0.98). CONCLUSIONS: It appears that patients can safely and effectively switch between generic warfarin and Coumadin.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".