The Problem With Predictions: A Cautionary Tale of Empirically Adjusting Apixaban Dosing With Carbamazepine
Bibliographic record
Abstract
Concomitant use of apixaban and carbamazepine (CBZ) is not recommended due to an anticipated reduction in apixaban concentration, although few case reports describe this interaction. We report a case of initiating apixaban 10 mg twice daily (BID), in a patient stabilized on CBZ 600 mg BID that was guided by prior experience. Apixaban concentrations were substantially elevated with initial empiric dosing; apixaban dosing of 7.5 mg BID was eventually implemented. This case highlights the fact that the degree of induction by CBZ can vary, regardless of the dose, and requires clinicians to be cautious when applying prior experiences with patients to new 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 | 0.032 | 0.247 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.017 | 0.050 |
| Insufficient payload (model declined to judge) | 0.007 | 0.009 |
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".