Cancer antigen-125 and risk of atrial fibrillation: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Cancer antigen-125 (Ca-125) is traditionally recognised as a tumour marker and its role in cardiovascular diseases has been studied only in recent years. Whether Ca-125 is elevated in patients with atrial fibrillation (AF) and its levels predict the risk of AF remains controversial. Therefore, we conducted a systematic review and meta-analysis of the association between Ca-125 levels and AF. Methods PubMed and EMBASE databases were searched until 1 June 2017 for studies that evaluated the association between Ca-125 and AF. Inclusion criteria included studies that compare Ca-125 in patients with and without AF, or those reporting HRs/ORs for risk of AF stratified by Ca-125 levels. Results A total of 39 entries were retrieved from the databases, of which 10 studies were included in the final meta-analysis. Ca-125 was significantly higher in patients with AF compared with those in sinus rhythm (mean difference=16 U/mL, 95% CI 2 to 30 U/mL, P<0.05; I2: 98%). Ca-125 significantly increased the risk of AF (HR: 1.39, 95% CI 1.06 to 1.82, P<0.05; I2: 84%). Conclusion Ca-125 was significantly higher in patients with AF than in those in sinus rhythm, and high Ca-125 is predictive of AF occurrence. However, the high heterogeneity observed means there is an uncertainty in the relationship between Ca-125 and AF, which needs to be confirmed by larger prospective studies.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| 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".