Risk of recurrent cancer-associated venous thromboembolism: A Danish nationwide cohort study
Bibliographic record
Abstract
BACKGROUND: Predictive factors for recurrent cancer-associated venous thromboembolism have been inconsistent across previous studies. To provide data for improved risk stratification, we described the risk of recurrent venous thromboembolism overall and across age, sex, calendar period, cancer type, Ottawa risk score, cancer stage, and cancer treatment in a nationwide cohort of patients with active cancer. METHODS: Using Danish administrative registries, we identified a cohort of all adult patients with active cancer and a first-time diagnosis of venous thromboembolism during 2003-2018. We accounted for the competing risk of death and calculated absolute risks of recurrent venous thromboembolism at six months. RESULTS: The population included 34,072 patients with active cancer and venous thromboembolism. Recurrence risks at six months were higher for patients with genitourinary cancer (6.5%), lung cancer (6.1%), gastrointestinal cancer (5.6%), brain cancer (5.2%), and hematological cancer (5.1%) than for patients with gynecological cancer (4.7%), breast cancer (4.1%), and other cancer types (4.8%). Recurrence risks were similar for men (5.2%) and women (4.9%), with and without chemotherapy (5.1%), across Ottawa risk score group (low: 5.0%; high: 5.1%) and across calendar periods but increased with increasing cancer stage. The overall six-month all-cause mortality risk was 26%, and highest for patients with lung cancer (49%) and lowest among breast cancer patients (4.1%). CONCLUSIONS: Six-month recurrence risk after first-time cancer-associated venous thromboembolism was high and varied by cancer type and patient characteristics. Refining risk stratification for recurrence may improve decision-making regarding treatment duration after cancer-associated thromboembolism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".