The Factor XII −4C>T Variant and Risk of Common Thrombotic Disorders: A HuGE Review and Meta-Analysis of Evidence From Observational Studies
Bibliographic record
Abstract
Coagulation factor XII is involved in thrombus formation and therefore may play a role in the etiology of thrombotic disorders. A common variant in the factor XII (F12) gene (-4C>T, rs1801020) results in decreased plasma levels of this coagulation factor. The existence of associations between low factor XII levels or F12 variants and thrombotic outcomes has been debated for more than a decade. The authors conducted a review and meta-analysis to evaluate the evidence for an association between F12 -4C>T and 2 common thrombotic outcomes: venous thromboembolism and myocardial infarction, which are hypothesized to share some etiologic pathways. MEDLINE, EMBASE, and HuGE Navigator were searched through July 2009 to identify relevant epidemiologic studies, and data were summarized using random-effects meta-analysis. Sixteen candidate gene studies (4,386 cases, 40,089 controls) were analyzed. None of the investigated contrasts reached statistical significance at P < 0.05, apart from a very weak association with myocardial infarction for the TT + CT versus CC contrast (odds ratio = 1.13, 95% confidence interval: 1.00, 1.27). Overall, based on the synthesis of observational studies, the evidence for an association between F12 -4C>T and venous thromboembolism and myocardial infarction is weak.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".