Survival Implications of Thrombus Recurrence or Bleeding in Cancer Patients Receiving Anticoagulation for Venous Thromboembolism Treatment
Bibliographic record
Abstract
Background Study aims were to analyze prospectively collected data from patients with cancer-associated venous thromboembolism (VTE) to determine the impact of VTE recurrence and anticoagulant-related bleeding on all-cause mortality. Patients/Methods Consecutive cancer patients with acute VTE treated with anticoagulants (March 1, 2013–November 30, 2021) were included in this analysis. Anticoagulant therapy-associated VTE recurrences, major bleeding, and clinically relevant nonmajor bleeding (CRNMB) were assessed for their impact on all-cause mortality outcomes. Results This study included 1,812 cancer patients with VTE. Of these, there were 97 (5.4%) with recurrent VTE, 98 (5.4%) with major, and 104 (5.7%) with CRNMB while receiving anticoagulants. Recurrent VTE (hazard ratio [HR]: 1.52; 95% confidence interval [CI]: 1.16–2.00; p = 0.0028), major bleeding (HR: 1.82; 95% CI: 1.41–2.31; p = 0.006), and CRNMB (HR; 1.38; 95% CI: 1.05–1.81; p = 0.018) each adversely influenced mortality outcomes. Deep vein thrombosis as the incident thrombotic event type was associated with VTE recurrence (HR: 1.78; 95% CI: 1.08–2.89; p = 0.02). Neither cancer type nor stage, chemotherapy, or Ottawa risk category influenced VTE recurrence. Higher body weights (HR: 1.01; 95% CI: 1.00–1.01; p = 0.005) were associated with increased major bleeding, while high Ottawa scores (HR: 0.66; 95% CI: 0.46–0.96; p = 0.03) and apixaban treatment (HR: 0.62; 95% CI: 0.45–0.84; p = 0.002) were associated with fewer major bleeding outcomes. Conclusion Among cancer patients receiving anticoagulant therapy for VTE, adverse outcomes such as VTE recurrence, major bleeding, or CRNMB increase mortality risk by 40 to 80%. Identifying variables predicting these outcomes may help risk-stratify patients with poor prognosis.
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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 | 0.007 |
| 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 | 0.001 | 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".