Prevalence of and Risk Factors for Venous Thromboembolism in Patients With Lymphoma: A Meta-Analysis
Bibliographic record
Abstract
PROBLEM IDENTIFICATION: The risk of venous thromboembolism (VTE) in patients with lymphoma may be overlooked because patients often experience thrombocytopenia from the disease or chemotherapy. A meta-analysis was conducted to identify the prevalence of and risk factors for VTE in patients with lymphoma. LITERATURE SEARCH: A systematic search of Embase®, Web of Science, PubMed®, and Cochrane Library databases was conducted to identify relevant studies investigating VTE in patients with lymphoma. DATA EVALUATION: The methodologic quality of the eligible observational studies was assessed using the Newcastle-Ottawa Scale. Stata, version 12.0, was used to perform the meta-analysis. SYNTHESIS: Female sex, older age, history of VTE, a diagnosis of diffuse large B-cell lymphoma, Ann Arbor stage III-IV disease, a higher performance status score, bulky disease, central nervous system involvement, a white blood cell count greater than 11 × 109/L, a D-dimer level greater than 0.5 mg/L, central venous catheterization, and treatment with doxorubicin were significant risk factors for VTE. IMPLICATIONS FOR PRACTICE: This meta-analysis identified risk factors for VTE, which may provide a theoretical foundation for clinical staff to conduct early assessment and identification of high-risk VTE groups, allowing for timely intervention.
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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 | 0.017 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.057 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".