Superwarfarin Exposure: An Important Uncommon Cause of Painless Bleeding
Bibliographic record
Abstract
Painless bleeding in a patient presenting from the community with elevated coagulation studies rarely makes the physicians suspect superwarfarin or rodenticide poisoning. Although a significant number of superwarfarin exposure cases are diagnosed every year, we believe there appears to be delay in diagnosis and confusion in determining what is the ideal way to treat and monitor these patients during the management. This is the first thorough literature review of all the reported cases of superwarfarin poisoning which also studied the clinical presentation, management and follow-up patterns. We present a 70-year-old man who presented to the emergency room with epistaxis, melena, cola-colored urine with elevated prothrombin time (PT), activated partial thromboplastin time (aPTT) and international normalized ratio (INR). Mixing studies showed complete correction of coagulopathy indicative of factor deficiency. Additional history revealed that the patient had arguments with family member at home and made us suspect superwarfarin exposure. Qualitative brodifacoum testing was positive and was managed with fresh frozen plasma and high doses of vitamin K1 (phytomenadione) with serial monitoring of INR and clinical symptoms. Superwarfarin poisoning should be considered in the differential diagnosis of a patient who presents with above clinical and laboratory profile especially in the absence of any history of coagulopathy or anticoagulant use. We want to raise public and especially physician awareness that history taking, early diagnosis and managing in right clinical setting play a significant role in survival of these patients.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".