Magnesium for Prevention of New-onset Postoperative Atrial Fibrillation Following Cardiac Surgery: A Systematic Review and Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: This meta-analysis article aimed to investigate the efficacy of magnesium in preventing new-onset postoperative atrial fibrillation (POAF). Methods: We searched Medline, Embase, Web of Science and Cochrane Library without any language or publication date restriction up to August 2023. We included randomized controlled trials (RCTs) that enrolled adults undergoing cardiac surgery without a history of atrial fibrillation, exploring the effect of magnesium supplementation in preventing new-onset POAF. We assessed the risk of bias using the Cochrane Risk of Bias 2.0 (RoB 2.0) tool. We conducted a random-effects meta-analysis using R and assessed the certainty of the evidence. Results: A total of 24 RCTs with 3,373 participants were included. We found that magnesium may reduce the risk of POAF compared to the control group (relative risk [RR]: 0.55; 95% confidence interval [CI]: 0.41, 0.74; low certainty). The subgroup analysis for trials with low/some concerns risk of bias showed that magnesium reduces the risk of new-onset POAF compared to control (RR: 0.70 [95% CI: 0.58, 0.84]; high certainty). Magnesium consumption had no significant effect on all-cause mortality (RR: 1.00 [95% CI: 0.34, 2.90]) or days of hospitalization (mean difference: -0.34 [95% CI: -0.94, 0.26]). Conclusion: The evidence indicates that magnesium administration reduces the incidence of new-onset POAF.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.041 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".