Steroids prevent early recurrence of atrial fibrillation following catheter ablation: a systematic review and meta-analysis
Bibliographic record
Abstract
Previous studies have reported that steroids may reduce the risk of atrial fibrillation (AF) recurrence after catheter ablation, but data regarding this issue have been controversial. Therefore, we conducted a meta-analysis of randomized clinical trials (RCTs) and observational studies to ascertain the association of steroids and AF recurrence after ablation. PubMed, Embase, and Cochrane online databases were searched from inception to December 2017. The primary outcome of the meta-analysis was short-term or long-term AF recurrence following a single ablation procedure with or without the use of steroids. Both fixed- and random-effects models were used to calculate the overall effect estimates. Eight studies (four RCTs and four observational studies), with a total 992 patients, were included in the present study. Our meta-analysis shows that steroid use was associated with reduced AF occurrence at 3 months (odd ratio (OR) = 0.53, 95% confidence interval (CI) = 0.31–0.90, P=0.02) and 12–14 months (OR = 0.67, 95% CI = 0.47–0.95, P=0.02) after radiofrequency (RF) catheter ablation (RFCA). No clear benefit was observed for AF recurrence at 2–3 days, 1 or 24 months of follow-up. Steroid use was associated with decreased risk of early AF recurrence 3 and 12–14 months after ablation. No clear relationship was observed for 2–3 days, 1 and 24 months of follow-up and further data are needed to clarify these results.
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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 | 0.036 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".