Warfarin and acetaminophen interaction: a summary of the evidence and biologic plausibility
Bibliographic record
Abstract
Ms TS is a 66-year-old woman who receives warfarin for prevention of systemic embolization in the setting of hypertension, diabetes, and atrial fibrillation. She had a transient ischemic attack about 4 years ago when she was receiving aspirin. Her INR control was excellent; however, over the past few months it has become erratic, and her average dose required to maintain an INR of 2.0 to 3.0 appears to have decreased. She has had back pain over this same period and has been taking acetaminophen at doses at large as 650 mg four times daily, with her dose varying based on her symptoms. You recall a potential interaction and wonder if (1) her acetaminophen use is contributing to her loss of INR control, and (2) does this interaction place her at increased risk of warfarin-related complications?
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".