Impact of Inherited Thrombophilia on Venous Thromboembolism in Children
Bibliographic record
Abstract
BACKGROUND: The aim of the present study was to estimate the impact of inherited thrombophilia (IT) on the risk of venous thromboembolism (VTE) onset and recurrence in children by a meta-analysis of published observational studies. METHODS AND RESULTS: A systematic search of electronic databases (Medline, EMBASE, OVID, Web of Science, The Cochrane Library) for studies published from 1970 to 2007 was conducted using key words in combination as both MeSH terms and text words. Citations were independently screened by 2 authors, and those meeting the inclusion criteria defined a priori were retained. Data on year of publication, study design, country of origin, number of patients/controls, ethnicity, VTE type, and frequency of recurrence were abstracted. Heterogeneity across studies was evaluated, and summary odds ratios and 95% CIs were calculated with both fixed-effects and random-effects models. Thirty-five of 50 studies met inclusion criteria. No significant heterogeneity was discerned across studies. Although >70% of patients had at least 1 clinical risk factor for VTE, a statistically significant association with VTE onset was demonstrated for each IT trait evaluated (and for combined IT traits), with summary odds ratios ranging from 2.63 (95% CI, 1.61 to 4.29) for the factor II variant to 9.44 (95% CI, 3.34 to 26.66) for antithrombin deficiency. Furthermore, a significant association with recurrent VTE was found for all IT traits except the factor V variant and elevated lipoprotein(a). CONCLUSIONS: The present meta-analysis indicates that detection of IT is clinically meaningful in children with, or at risk for, VTE and underscores the importance of pediatric thrombophilia screening programs.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.019 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".