The Association between Oral Anticoagulants and Cancer Incidence among Individuals with Nonvalvular Atrial Fibrillation
Bibliographic record
Abstract
OBJECTIVE: Existing evidence on the association between vitamin K antagonists (VKAs) and direct oral anticoagulants (DOACs) and cancer is limited and contradictory. No observational studies have been conducted to simultaneously address the cancer safety of VKAs and DOACs. The objective of this study was to determine whether use of VKAs and DOACs, separately, when compared with nonuse, is associated with cancer overall and prespecified site-specific incidence. METHODS: Using the United Kingdom Clinical Practice Research Datalink, we identified patients newly diagnosed with nonvalvular atrial fibrillation (NVAF) between 2011 and 2017. Using a time-varying exposure definition, each person-day of follow-up was classified as use of (1) VKAs, (2) DOACs, (3) VKAs and DOACs (drug switchers), and (4) nonuse of anticoagulants (reference). We also conducted a head-to-head comparison of new users of DOACs versus VKAs using propensity score fine stratification weighting. Hazard ratios (HRs) with 95% confidence intervals (CIs) for cancer overall and prespecified subtypes were estimated using Cox proportional hazards models. RESULTS: Compared with nonuse, use of VKAs was not associated with cancer overall (HR: 1.05, 95% CI: 0.91-1.22) or cancer subtypes. Similarly, use of DOACs was not associated with cancer overall (HR: 1.13, 95% CI: 0.93-1.37), but an association was observed for colorectal cancer (HR: 1.73, 95% CI: 1.01-2.99), and pancreatic cancer generated an elevated, though nonsignificant HR (HR: 2.15, 95% CI: 0.72-6.44). Results were consistent in the head-to-head comparison. CONCLUSION: Use of oral anticoagulants is not associated with the incidence of cancer overall among patients with NVAF. Possible associations between DOACs and colorectal and pancreatic cancer warrant further study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".