A39 NON-VITAMIN K ANTAGONIST ORAL ANTICOAGULANTS AND GASTROINTESTINAL BLEEDING: A NETWORK META-ANALYSIS
Bibliographic record
Abstract
*Drs. Alastair Dorreen and Corey Miller are co-first authors Several non-vitamin K antagonist oral anticoagulants (NOACs) have been approved for clinical use. A recent meta-analysis of randomized controlled trials (RCTs) and some observational data have suggested an increased risk of gastrointestinal bleeding (GIB) with dabigatran and rivaroxaban compared to conventional anticoagulation, yet data regarding comparative risk of GIB between NOACs are limited. To conduct a network meta-analysis to assess the comparative risk of GIB between NOACs. An initial search for RCTs comparing NOACs to conventional anticoagulation therapy was performed using the EMBASE, Medline, Cochrane and ISI Web of knowledge databases through January 2017. Trials assessing NOACs for the treatment of acute coronary syndrome and other unapproved indications were excluded. The primary outcome of comparison was the risk of GIB via a network meta-analysis with random effects model using the netmeta package in R 3.2 (www.r-project.org). A total of 51 trials were included, randomizing 180,853 patients. When comparing dabigatran vs. apixaban (OR: 1.72, 95% CI: 0.90; 3.30), dabigatran vs. edoxaban (OR: 1.32, 95% CI: 0.66; 2.64), dabigatran vs. rivaroxaban (OR: 1.11, 95% CI: 0.60; 2.04), rivaroxaban vs. apixaban (OR: 1.55, 95% CI: 0.84; 2.87), rivaroxaban vs. edoxaban (OR: 1.19, 95% CI: 0.61; 2.33) and apixaban vs. edoxaban (OR: 0.77, 95% CI: 0.38; 1.55), there was no significant difference in odds of major GIB. Secondary analysis did not reveal any difference in the odds of major GIB when comparing individual NOACs to warfarin, low-dose enoxaparin, placebo or aspirin. Overall, the risk of major GIB was equivalent between NOACs when compared head-to-head in a network meta-analysis. Further high-quality studies are needed to characterize GIB risk among individual NOACs. None
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.050 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".