Impact of ABO Blood Group on the Development of Venous Thromboembolism in Children With Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Few studies have evaluated the impact of ABO blood group on the risk of venous thromboembolism (VTE) in pediatric populations. We performed a systematic review to determine whether children (0 to 18 y old) with non-O blood group have an increased risk of developing VTE compared with those with O blood group. METHODS: We searched Ovid Medline, Embase, PubMed, Cochrane, Web of Science, and CINAHL online databases from inception to December 2018 to find studies involving blood grouping and VTE. Data was collected regarding patients' underlying diseases, sex, age, ABO blood group, and VTE frequency. A meta-analysis using the random effect model was performed, and heterogeneity was assessed with the I2 value. RESULTS: Among 1280 unique articles identified, 7 studies (3 conference abstracts, 4 peer-reviewed journal articles) involving 609 VTE cases were included in the systematic review. Six studies were eligible for a meta-analysis; all involved patients with cancer. Blood group O was protective against VTE (odds ratio, 0.56; 95% confidence interval, 0.43-0.79). Findings were similar across sensitivity analyses. CONCLUSIONS: This systematic review has documented that O blood group is protective against new-onset VTE in children with cancer. Larger studies across different ethnic backgrounds and disease categories are needed to confirm these findings.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.008 | 0.009 |
| 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".