Global mortality of children after perioperative cardiac arrest: A systematic review, meta-analysis, and meta-regression
Bibliographic record
Abstract
BACKGROUND: The body of evidence showed that perioperative cardiac arrest and mortality trends varied globally over time particularly in low and middle-income nations. However, the survival of children after cardiac arrest and its independent predictors are still uncertain and a topic of debate. This study was designed to investigate the mortality of children after a perioperative cardiac arrest based on a systematic review of published peer-reviewed literature. METHODS: A comprehensive search was conducted in PubMed/Medline; Science direct, CINHAL, and LILACS from December 2000 to August 2021. All observational studies reporting the rate of perioperative CA among children were included. The data were extracted with two independent authors in a customized format. The methodological quality of the included studies was evaluated using the Newcastle-Ottawa appraisal tool. RESULTS: A total of 397 articles were identified from different databases. Thirty-eight studies with 3.35 million participants were included. The meta-analysis revealed that the global incidence of perioperative cardiac arrest was 2.54(95% CI: 2.23 to 2.84) per 1000 anesthetics. The global incidence of perioperative mortality was 41.18 (95% CI: 35.68 to 46.68) per 1000 anesthetics. CONCLUSION: The incidence of anesthesia-related pediatric cardiac arrest and mortality is persistently high in the last twenty years in low and middle-income countries. This probes an investment in continuous medical education of the perioperative staff and adhering with the international standard operating protocols for common procedures and critical situations. REGISTRATION: This systematic review and meta-analysis is registered in the research registry (UIN: researchregistry6932).
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.051 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".