Transforming Growth Factor-Beta3 Gene Polymorphisms and Nonsyndromic Cleft Lip and Palate Risk: A Meta-Analysis
Bibliographic record
Abstract
AIMS: To investigate the association between transforming growth factor-beta3 (TGF-β3) genetic polymorphisms and nonsyndromic cleft lip and palate (NSCLP) risk. METHODS: An extensive literature search for relevant studies was conducted on PubMed, Embase, Web of Science, Cochrane Library, and CBM databases from their inception through June 1st, 2013. Case-control studies addressing the correlation between TGF-β3 gene polymorphisms and NSCLP risk. The genotype distribution of the controls should conform to Hardy-Weinberg equilibrium. The quality of the included studies was assessed independently by two authors based on the Newcastle-Ottawa scale. All analyses were calculated using the STATA 12.0 software. RESULTS: The association between TGF-β3 gene polymorphisms and NSCLP risk was assessed. Eleven case-control studies were included with a total of 1601 NSCLP cases and 1463 healthy controls. Our meta-analysis results indicated that mutant variants of the TGF-β3 gene may be associated with an increased risk of NSCLP, especially among Asian populations. Further subgroup analyses also revealed significant associations between mutant variants of the TGF-β3 gene and an increased risk of NSCLP in the population-based and polymerase chain reaction-restriction fragment length polymorphism studies. Meta-regression analyses showed that ethnicity may be a major source of heterogeneity. CONCLUSION: Our meta-analysis suggests that TGF-β3 gene polymorphisms may contribute to NSCLP susceptibility, especially among Asian populations.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.043 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".