Bleeding after endoscopic resection between direct oral anticoagulants or warfarin: Systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background and Aim Oral anticoagulants are risk factors for post‐endoscopic resection bleeding. We aimed to conduct a systematic review and meta‐analysis for the risks of post‐procedural bleeding (PPB) for direct oral anticoagulants (DOACs) and warfarin following endoscopic resection. Methods Two independent reviewers searched PubMed, Web of Science, Embase, and Cochrane Library. The Newcastle–Ottawa Scale score was used to assess the quality of the studies, the pooled odds ratio (OR) to present PPB results, and the funnel plots to assess publication bias. The Higgins I 2 statistic was employed to determine the variation across studies due to heterogeneity. Results We reviewed 30 articles. PPB occurred in 586 patients on DOACs and 1782 on warfarin. The patients on DOACs had a significantly lower overall risk of PPB compared with those on warfarin (OR, 0.867, 95% confidence interval, 0.771–0.975; P = 0.017, I 2 = 1.6%). Cumulative meta‐analysis showed that the PPB rate of DOACs has the trend to be lower than that of warfarin with publication year and sample size. For the subgroup of endoscopic submucosal dissection, the PPB of DOACs was significantly lower than that of warfarin (OR, 0.786; 95% confidence interval, 0.633–0.976; P = 0.029, I 2 = 0%). No significant difference was observed between DOACs and warfarin for anticoagulant strategies, endoscopic procedures, and lesion location. Conclusions Compared with warfarin, DOACs have the possibility to significantly decrease the risk of PPB following endoscopic resection, especially for endoscopic submucosal dissection.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".