Association between KCNE1 G38S gene polymorphism and risk of atrial fibrillation
Bibliographic record
Abstract
BACKGROUND: Previous case-control studies on association between KCNE1 G38S polymorphism and risk of atrial fibrillation (AF) have been published but because of the conflicting results and small sample size of individual studies, the consolidated result is still controversial. OBJECTIVES: The aim of this study was to explore the relationship between KCNE1 G38S polymorphism and risk of AF. METHODS: We performed a comprehensive literature search on PubMed, Embase, OVID, Web of Science, Wan Fang, and CNKI databases up to March 10, 2017 in English and Chinese languages. Two of the authors individually extracted study data and assessed the study quality using Newcastle-Ottawa scale. Odds ratios (ORs) and 95% confidence intervals (CIs) were combined in different genetic models for evaluation using a random-effect model or fixed-effect model according to interstudy heterogeneity. RESULTS: There were totally 14 independent case-control studies of 2810 patients and 3080 healthy controls included. Significant associations were found between KCNE1 G38S polymorphism and AF in overall population under all genetic models: allelic (OR: 1.34, 95% CI: 1.24-1.45, P < .001), homozygous (OR: 1.90, 95% CI: 1.61-2.24, P < .001), heterozygous (OR: 1.43, 95% CI: 1.21-1.68, P < .001), recessive (OR: 1.42, 95% CI: 1.20-1.69, P < .001), dominant genetic model (OR: 1.62, 95% CI: 1.39-1.89, P < .001). Subgroup analyses indicated similar association in Chinese and white. CONCLUSIONS: The G38S polymorphism in the KCNE1 gene can significantly increase the risk of AF in both Chinese and white.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".