Risk factors for gastrointestinal bleeding in patients with gastrointestinal cancer using edoxaban
Bibliographic record
Abstract
BACKGROUND: In the Hokusai VTE Cancer study, the risk of major bleeding was 2.9% higher in the edoxaban group compared with the dalteparin group, mainly due to more gastrointestinal bleedings in patients with gastrointestinal cancer. The identification of risk factors for gastrointestinal bleeding may help to guide the use of DOACs in these patients. OBJECTIVES: To evaluate risk factors for gastrointestinal bleeding in patients with gastrointestinal cancer receiving edoxaban. PATIENTS/METHODS: In this nested case-control study in patients with gastrointestinal cancer randomized to edoxaban in the Hokusai VTE Cancer study, cases (patients with clinically relevant gastrointestinal bleeding during treatment) were randomly matched to three controls (patients who had no gastrointestinal bleeding). Data for the 4-week period prior to bleeding were retrospectively collected. Odds ratios (ORs) were calculated in a crude conditional logistic regression model and a multivariable model adjusted for age, sex, and cancer type. RESULTS: Twenty-four cases and 64 matched controls were included. In the multivariable analysis, advanced cancer, defined as regionally advanced or metastatic cancer (OR 3.6, 95% CI 1.01-12.6) and low hemoglobin levels (OR 4.8, 95% CI 1.5-16.0) were significantly associated with bleeding. There was no significant difference in patients with resected tumors (OR 0.4, 95% CI 0.1-1.4), or in patients on chemotherapy (OR 1.3, 95% CI 0.5-3.5). CONCLUSION: Advanced cancer and low hemoglobin levels were associated with an increased risk of gastrointestinal bleeding in patients with gastrointestinal cancer receiving edoxaban. We were unable to identify other risk factors, mainly due to limited statistical power.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".