New-onset atrial fibrillation in sepsis is associated with increased morbidity and mortality
Bibliographic record
Abstract
BACKGROUND: The development of new-onset atrial fibrillation in sepsis has been associated with adverse outcomes. METHODS: A systematic literature search was conducted to retrieve articles that investigated the association of new-onset atrial fibrillation in patients diagnosed with sepsis. The primary outcome of interest was the pooled risk ratio (RR) of in-hospital mortality in patients with new-onset atrial fibrillation and sepsis. RESULTS: Six studies included 3100 patients with new-onset atrial fibrillation in sepsis and 36,900 patients without new-onset atrial fibrillation in sepsis. The pooled RR for in-hospital mortality was 1.45 (95 % CI 1.32-1.60, p < 0.00001, I (2 = )24 %). New-onset atrial fibrillation was also associated with increased ICU mortality, ICU and in-hospital length of stay and stroke. New-onset atrial fibrillation occurred more in the elderly, those with a prior history of cardiovascular and respiratory disease, and those with increased severity of illness. CONCLUSION: Prospective randomised trials are needed to clarify the significance of new-onset atrial fibrillation in sepsis, optimal treatment strategies for these patients, and the benefit of systemic anticoagulation. Physicians should be aware that new-onset atrial fibrillation in sepsis is not merely an observed temporary arrhythmia but a marker of poor prognosis and should be managed accordingly.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".