The Association of Malignancy With Stroke and Bleeding in Atrial Fibrillation
Bibliographic record
Abstract
BACKGROUND: It is undetermined if malignancy independently increases stroke risk in atrial fibrillation (AF). OBJECTIVES: This study sought to determine the association of malignancy with stroke and bleeding in AF. METHODS: -VASc score, and ATRIA bleeding score. Outcomes included hospitalizations for stroke and hospitalization/emergency visits for bleeding. Cause-specific regression was used to determine the HR for malignancy after adjusting for time-varying anticoagulation status. Analyses were repeated for specific subgroups of cancer patients (with matched control subjects). RESULTS: Among 199,710 AF patients, 24,991 (12.5%) people had prior malignancy. Malignancy was associated with more inpatient diagnoses of AF (vs outpatient) and less anticoagulation. We matched 43,802 people with AF (21,901 with malignancy, mean age 78.1 years; 59.5% male). After adjusting for anticoagulation status, malignancy had a similar hazard of stroke (HR: 1.01; 95% CI: 0.88-1.15) but higher hazard of bleeding (HR: 1.45; 95% CI: 1.37-1.53) compared with cancer-free control subjects in the matched sample. Analyses of cancer subgroups with comparison to matched control subjects mostly showed consistent results, except for: 1) increased hazard of stroke in lung cancer; and 2) lack of increased bleeding hazard in breast cancer and lymphoma. CONCLUSIONS: People with AF and malignancy generally had similar hazards of stroke but higher hazards of bleeding compared with cancer-free control subjects, suggesting that malignancy should not lower the threshold for anticoagulation in AF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".