Predictors of recurrent venous thromboembolism and bleeding in patients with cancer: a meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Patients with cancer and venous thromboembolism (VTE) have a high risk of recurrent VTE and anticoagulant-related bleeding. This study aimed to identify prognostic factors for these complications. METHODS: A systematic review was performed for randomized trials and cohort studies evaluating prognostic factors for recurrent VTE or anticoagulant-related bleeding in adult patients with cancer and VTE. Adjusted hazard ratios (aHRs) for factors were pooled using random-effects meta-analysis. The certainty of evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluation approach. RESULTS: Thirty-three studies (n = 96 753) were included in the meta-analyses. Factors with high certainty of association with increased risk of recurrent VTE included a previous history of VTE [aHR 1.50 (95% CI 1.08-2.09)], Eastern Cooperative Oncology Group (ECOG) performance status >0 [1.81 (1.34-2.46)] or >1 [2.44 (1.55-3.84)], advanced cancer [1.38 (1.15-1.65)], and specific cancer sites including lung [1.78 (1.29-2.46)], hepatobiliary [2.37 (1.70-3.30)], pancreas [3.20 (2.06-4.96)], and genitourinary [1.38 (1.14-1.67)]. Conversely, recent surgery [aHR 0.56 (95% CI 0.40-0.76)] and breast cancer [0.43 (0.23-0.81)] had a high certainty of association with a decreased risk. Factors with a high certainty of association with an increased risk of anticoagulant-related bleeding included a history of bleeding [aHR 2.41 (95% CI 1.50-3.88)], ECOG performance status ≥2 [2.10 (1.48-2.99)], advanced cancer [1.60 (1.29-1.97)], and cancers of the brain [2.25 (1.64-3.09)], gastrointestinal system [1.74 (1.44-2.11)], genitourinary system [1.90 (1.48-2.45)], and prostate [1.72 (1.26-2.34)]. CONCLUSIONS: The prognostic factors identified in this meta-analysis should be considered as part of risk stratification frameworks for anticoagulation management in patients with 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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.063 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.003 | 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".