Association between DEFB1 polymorphisms and periodontitis: a meta-analysis
Bibliographic record
Abstract
Previous studies showed that DEFB1 gene polymorphisms may impact the development and progression of periodontitis; nevertheless, inconsistent conclusions were described. This study meta-analytically explored the association between periodontitis the DEFB1 gene polymorphisms and periodontitis. We searched PubMed, Embase, Springer and Cochrane Library for the relevant case-control studies of periodontitis up to February 13th, 2019. Two reviewers selected studies according to the predefined inclusion and exclusion criteria. Newcastle-Ottawa Scale (NOS) was used to assess the quality of studies, and the combined effect size was calculated using R 3.12 software. A total of 9 studies involving 4113 patients and 2373 controls were included. Meta-analysis of DEFB1-G1654A gene polymorphisms showed that there were significant differences in model A vs. G (OR = 3.7876, 95%CI = 2.9051-4.9382, P < 0.001), AA vs. GG (OR = 4.6743, 95%CI = 3.0900-7.0710, P < 0.001), AA vs.GG + AG (OR = 3.5131, 95%CI = 2.4496-5.0384, P < 0.001), AA + AG vs. GG (OR = 4.3087, 95%CI = 2.8827-6.4402, P < 0.001) and AG vs. GG (OR = 3.0639, 95%CI = 1.6804-5.5863, P = 0.003). However, no significant differences were found between DEFB1 rs11362, rs1799946 and rs1800972 and periodontitis. Sensitivity analysis implied that our results were robust and no publication bias was noticed. Our meta-analysis showed that the DEFB1-G1654A polymorphism may be a genetic susceptibility factor for periodontitis.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.051 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".