A Case of a Reversible Neurologic Adverse Reaction to Apixaban Confirmed by Re-Challenge
Bibliographic record
Abstract
Post-marketing reporting of adverse drug events is essential for new medications, as pre-FDA approval studies lack sufficient subject numbers to detect signals for rare events. Prescriptions for the novel oral anticoagulant factor Xa inhibitors (rivaroxaban, apixaban, edoxaban) have equaled or exceeded those for vitamin K antagonists in many clinical settings requiring chronic anticoagulation, and those of injectable heparins for deep vein thrombosis prophylaxis. We report the case of a 60-year-old woman followed for permanent atrial fibrillation who was prescribed apixaban. She rapidly developed worsening neurologic symptoms of imbalance and non-vertiginous dizziness preventing her from walking, headache, diplopia, and confusion/disorientation. Her symptoms began to resolve after stopping the drug, with return to baseline function within 72 h. Unbeknownst to her cardiology care team, the patient chose to re-challenge herself with apixaban at the same dose, producing identical symptoms and again total symptom resolution within 24 h of drug discontinuation. When seen by her physician, her physical examination was unchanged from her pre-treatment baseline. Symptoms did not recur when switched to rivaroxaban therapy.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".