Apixaban, concomitant medicines and spontaneous reports of haemorrhagic events
Bibliographic record
Abstract
Introduction: Little is known about the potential safety issues associated with apixaban in clinical practice and their reporting in spontaneous adverse event (SAE) databases. Objective: To describe SAE reports associated with the oral anticoagulant apixaban from Australia, Canada and USA and to examine associated concomitant medicine use. Methods: SAE report databases from Australia, Canada and the USA were examined for all reports of adverse events associated with apixaban and concomitant medicines from 1 January 2012 to 30 September 2014. Disproportionality analysis (proportional reporting ratio (PRR) and reporting odds ratio (ROR)) was conducted for the quantitative detection of signals using the USA database. Results: There were 97 SAE reports associated with apixaban from Australia, 77 from Canada and 2877 from the USA. Reporting of haemorrhage (any type) was common, ranging from 18% for USA to 31% for Australia. Gastrointestinal (GI) haemorrhage was the most commonly reported haemorrhage, accounting for approximately 10% of adverse event reports across all countries. Positive signals were confirmed in the USA data (haemorrhage (any type) PRR, 12.1; χ 2 , 5582.2 and ROR, 13.4; 95% CI: 12.13–14.6; GI haemorrhage PRR, 11.8; χ 2 , 2325.4 and ROR, 12.3; 95% CI, 10.8–14.0). Reporting of concomitant use of medicines with the potential to increase bleeding risk ranged from 47.6% in Canada to 65.5% in Australia. Conclusion: A large proportion of adverse event reports for apixaban were associated with use of concomitant medicines which may have increased the risk of haemorrhage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".