Bibliographic record
Abstract
BACKGROUND: Warfarin is a highly efficacious oral anticoagulant, but its use is limited by a well-founded fear of bleeding. Drug and food interactions are frequently cited as causes of adverse events with warfarin. We provide an updated systematic overview of the quality, clinical effect, and importance of these reported interactions. DATA SOURCES: MEDLINE, TOXLINE, IPA, and EMBASE databases from October 1993 to March 2004. Database searches combined the keyword warfarin with drug interactions, herbal medicines, Chinese herbal drugs, and food-drug interactions. STUDY SELECTION: Eligible articles contained original reports of warfarin drug or food interactions in human subjects. Non-English articles were included if sufficient information could be abstracted. DATA EXTRACTION: Reports were rated independently by 2 investigators for interaction direction, clinical severity, and quality of evidence. Quality of evidence was based on previously validated causation criteria and study design. DATA SYNTHESIS: Of 642 citations retrieved, 181 eligible articles contained original reports on 120 drugs or foods. Inter-rater agreement was excellent, with weighted kappa values of 0.84 to 1.00. Of all reports, 72% described a potentiation of warfarin's effect and 84% were of poor quality, 86% of which were single case reports. The 31 incidents of clinically significant bleeding were all single case reports. Newly reported interactions included celecoxib, rofecoxib, and herbal substances, such as green tea and danshen. CONCLUSIONS: The number of drugs reported to interact with warfarin continues to expand. While most reports are of poor quality and present potentially misleading conclusions, the consistency of reports of interactions with azole antibiotics, macrolides, quinolones, nonsteroidal anti-inflammatory drugs, including selective cyclooxygenase-2 inhibitors, selective serotonin reuptake inhibitors, omeprazole, lipid-lowering agents, amiodarone, and fluorouracil, suggests that coadministration with warfarin should be avoided or closely monitored. More systematic study of warfarin drug interactions in patients is urgently needed.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.030 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".