Ethnic Differences in Genetic Ion Channelopathies Associated with Sudden Cardiac Death: A Systematic Review and Meta-Analysis.
Bibliographic record
Abstract
BACKGROUND AND AIMS: Reports of allele frequencies encoding ion channel, or their interacting proteins associated with sudden cardiac death among different ethnic groups have been inconsistent. Here, we aimed to characterize the distribution of these genes and their alleles among various ethnicities through meta-analysis. METHODS: among the Black, Caucasian, Asian, and Hispanic ethnicities. Searches in PubMed, Google Scholar, and Web of Science resulted in 18 reports published before July 2015 that met the eligible criteria. Allele frequencies were averaged by weight, and pooled values were calculated by inverse variance. Fixed-effects and random-effects models were used to pool effect sizes within each study and across different studies, respectively. Moreover, to extend our findings, we used sequenced genomic data from the Exome Aggregation Consortium to compare allele frequencies between different ethnicities. RESULTS: frequency (0.16%). Analysis of the Exome Aggregation Consortium also provided consistent data in agreement the meta-analysis. CONCLUSION: Overall, Asians carried the most alleles of genes associated with sudden cardiac death. The meta-analysis reveals significant differences in allele distribution of channelopathy-associated genes among different ethnic groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.018 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".