Acute Lower Gastrointestinal Bleeding in Patients Treated With Non-Vitamin K Antagonist Oral Anticoagulants Compared With Warfarin in Clinical Practice: Characteristics and Clinical Outcome
Bibliographic record
Abstract
BACKGROUND: Acute lower gastrointestinal bleeding (ALGIB) can occur in patients on anticoagulant therapy (either warfarin or non-vitamin K oral anticoagulants (NOACs)). Use of NOACs has been increasing compared to warfarin in recent years. We analyzed patients with ALGIB on anticoagulation therapy and compared characteristics, management and clinical outcome in patients treated with NOACs versus warfarin. METHODS: All patients with ALGIB on anticoagulation therapy treated in two (affiliated) centers during a 7-year period were evaluated. Characteristics and clinical outcome were compared between patients on warfarin and patients on NOACs. RESULTS: Out of the 587 patients identified with ALGIB during the study period, 43 (7.3%) were on NOACs and 68 (11.6%) on warfarin. Mean age was 75.9 ± 9.5 and 77.1 ± 7.9 years respectively. Site of bleeding was located in the small bowel in 2/43 of NOAC patients and 6/68 of warfarin group. Vascular ectasias (8/43 vs. 6/68, P = 0.010) and polyps/neoplasia (13/43 vs. 6/68, P = 0.025) were more commonly causes of bleeding in patients on NOACs. While endoscopic hemostasis was more commonly needed in patients on NOACs (17/43 vs. 14/68, P = 0.049), they required less hospitalization days (4.5 ± 3.6 vs. 6.1 ± 4.2, P = 0.032). Blood transfusions and need for other interventions (embolization and/or surgery) as well as recurrence of bleeding and mortality were not statistically different. CONCLUSIONS: Although NOAC patients with ALGIB exhibit some differences on certain clinical characteristics when compared to warfarin patients, they share a similar clinical outcome.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".