Leukemia and Risk of Venous Thromboembolism: A Meta-analysis and Systematic Review of 144 Studies Comprising 162,126 Patients
Bibliographic record
Abstract
Venous thromboembolism (VTE) has significant clinical implications in leukemia patients. However, the actual frequency of this complication remains unknown. We performed a systematic review and meta-analysis to better estimate the frequency of this complication and to assess the risk factors that contribute to its occurrence. We searched several databases, including PubMed, Embase, and Web of Science, and assessed study quality using the Newcastle-Ottawa scale. The pooled frequency of VTE in leukemia patients was calculated. A total of 144 studies met the eligibility criteria. The incidence rate (IR) of VTE from 72 prospective studies comprising 9,061 patients was 5% (95%CI: 4-6%). The incidence rate (IR) of VTE in ALL, CLL, total-AML, and CML population was 5% (95%CI: 4-6%), 3% (95%CI: 2-5%), 6% (95%CI: 4-8%) and 13% (95%CI: 1-36%). The incidence of VTE was markedly decreased among ALL patients who received anticoagulation treatment (IR: 1%, 95%CI: 0-6%) or concentrates therapy (IR: 3%, 95%CI: 0-9%). The overall incidence of VTE in the leukemia population was high, particularly in transplant recipients, who had the highest risk (IR: 8%, 95% CI: 4-13%). Prophylactic approaches could significantly decrease the occurrence of VTE events.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.031 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".