Long-Term Vitamin K Antagonists and Cancer Risk
Bibliographic record
Abstract
OBJECTIVES: Vitamin K antagonists (VKAs) remain one of the most commonly used anticoagulation therapies. The potential anticancer effect of long-term use of VKAs has been a matter of debate with conflicting results. Our goal was to perform a systematic review and meta-analysis examining the association between long-term VKAs use and cancer risk. METHODS: Systematic searches of multiple major databases were performed from inception until January 2018. We included studies of adults that compared incidence of any cancer between ≥6 months use of VKAs (long-term group) and <6 months use of VKAs or nonuse (control group). Primary outcome was all-cancer incidence and secondary outcomes were cancer-specific incidence, all-cause death and cancer-specific mortality. Hazard ratios (HRs) were pooled using a random-effects model, and individual studies were weighted using inverse variance. RESULTS: We identified 9 observational studies that included 1,521,408 patients. No randomized trials were identified. In comparison to control, long-term use of VKAs was associated with a significant reduction in incidence of all cancers (HR, 0.84; 95% confidence interval [CI], 0.81-0.88; P<0.001). In a prespecified subgroup analysis, long-term use of VKAs demonstrated a significant reduction in all-cancer incidence when compared with control in individuals whose indication for VKAs were venous thromboembolism (HR, 0.69; 95% CI, 0.52-0.90; P=0.007). CONCLUSIONS: The use of long-term VKAs, for any indication, is associated with lower cancer incidence. This finding could have important clinical implications for the choice of oral anticoagulation therapies among specific patients with a higher baseline risk of cancer.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.011 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".