Inherited thrombophilia gene mutations and risk of venous thromboembolism in patients with cancer: A systematic review and <scp>meta‐analysis</scp>
Bibliographic record
Abstract
In the general population, individuals with an inherited thrombophilia have a higher risk of thrombosis, but the effect of inherited thrombophilia on the risk of cancer-associated venous thromboembolism (VTE) remains controversial. Our objective was to determine the risk of VTE in cancer patients with inherited thrombophilia. We conducted a systematic review and meta-analysis of studies reporting on VTE after a cancer diagnosis in adult patients who were tested for inherited thrombophilia. In September 2022, we searched Medline, EMBASE, and Cochrane Central. Two reviewers screened the abstracts/full texts and assessed study quality using the Quality in Prognostic Studies tool. We used Mantel-Haenszel random-effects models to estimate pooled odds ratios (OR) of VTE and 95% confidence intervals (95%CI). We included 37 and 28 studies in the systematic review and meta-analysis, respectively. Most studies focused on specific cancer types and hematologic malignancies were rare. The risk of VTE was significantly higher in cancer patients with non-O (compared with O) blood types (OR: 1.56 [95% CI: 1.28-1.90]), Factor V Leiden, and Prothrombin Factor II G20210A mutations compared with wild types (OR: 2.28 [95% CI: 1.51-3.48] and 2.14 [95% CI: 1.14-4.03], respectively). Additionally, heterozygous and homozygous methylenetetrahydrofolate reductase C677T had ORs of 1.50 (95% CI: 1.00-2.24) and 1.38 (95% CI: 0.87-2.22), respectively. Among those with Plasminogen-Activator Inhibitor-1 4G/5G, Vascular Endothelial Growth Factor (VEGF) A C634G, and VEGF C2578A mutations, there was no significant association with VTE. In conclusion, this meta-analysis provided evidence that non-O blood types, Factor V Leiden, and Prothrombin Factor II G20210A mutations are important genetic risk factors for VTE in cancer patients.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".