A Meta-Analysis of Case Fatality Rates of Recurrent Venous Thromboembolism and Major Bleeding in Patients with Cancer
Bibliographic record
Abstract
BACKGROUND: Knowing the case fatality rates of recurrent venous thromboembolism (VTE) and major bleeding is important for weighing the relative risks and benefits of anticoagulation and deciding on the duration of anticoagulant therapy, but these rates are uncertain in patients with cancer-associated thrombosis. METHODS: We performed a systematic review and a meta-analysis to determine the incidence of recurrent VTE and major bleeding and their respective case fatality rates in patients with cancer-associated VTE. RESULTS: Our analysis included 29 studies (15 prospective cohort studies and 14 randomized controlled trials) from 1980 to January 2019. Data from 8,000 cancer patients with 4,786 patient-years of follow-up were summarized. Rates of recurrent VTE and fatal recurrent VTE were 23.7 (95% confidence interval [CI]: 20.1-27.8) and 1.9 (95% CI: 0.8-4.0) per 100 patient-years of follow-up, respectively, with a case fatality rate of 14.8% (95% CI: 6.6-30.1%). The rates of major bleeding and fatal major bleeding events were 13.1 (95% CI: 10.3-16.7) and 0.8 (95% CI: 0.3-2.1) per 100 patient-years of follow-up, respectively, with a case fatality rate of 8.9% (95% CI: 3.5-21.1%). While the estimates of case fatality vary by anticoagulation regimen and study design, the differences between them were not statistically significant. CONCLUSION: In cancer patients receiving anticoagulation, the case fatality rate of recurrent VTE is higher than the case fatality rate of major bleeding. These findings may help to inform decisions regarding the management of anticoagulation in patients with active cancer and VTE.
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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 | 0.021 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.056 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".