Magnesium prophylaxis of new-onset atrial fibrillation: A systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: Atrial fibrillation (AF) is the most common cardiac arrhythmia in intensive care units (ICU) and is associated with increased morbidity and mortality. Magnesium prophylaxis has been shown to reduce incidence of AF in cardiac surgery patients, however, evidence outside this population is limited. The objective of this study is to summarize studies examining magnesium versus placebo in the prevention of NOAF outside the setting of cardiac surgery. SOURCE: We performed a comprehensive search of MEDLINE, EMBASE, and Cochrane Library (CENTRAL) from inception until January 3rd, 2023. We included all interventional research studies that compared magnesium to placebo and excluded case reports and post cardiac surgery patients. We conducted meta-analysis using the inverse variance method with random effects modelling. PRINCIPAL FINDINGS: Of the 1493 studies imported for screening, 87 full texts were assessed for eligibility and six citations, representing five randomized controlled trials (n = 4713), were included in the review, with four studies (n = 4654) included in the pooled analysis. Administration of magnesium did not significantly reduce the incidence of NOAF compared to placebo (OR 0.72, [95% CI 0.48 to 1.09]). CONCLUSION: Use of magnesium did not reduce the incidence of NOAF, however these studies represent diverse groups and are hindered by significant bias. Further studies are necessary to determine if there is benefit to magnesium prophylaxis for NOAF in non-cardiac surgery patients.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.006 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".