A systematic review and meta‐analysis of acute severe complications of pediatric anesthesia
Bibliographic record
Abstract
BACKGROUND: Quantification of acute severe complications of pediatric anesthesia is essential to plan clinical guidelines and educational curricula. AIM: Our aim was to identify complications in terms of frequency and outcomes. METHODS: We defined acute severe complications as an unexpected perioperative event, which without intervention by the anesthesiologist within 30 min may lead to disability or death. A systematic search was performed using MEDLINE, EMBASE, and CINAHL. Screening and data extraction were performed independently. Assessment of bias was conducted using GRADE guidelines. RESULTS: Of 3002 abstracts, 25 met all inclusion criteria. The most common acute severe complications in pediatric anesthesia are related to airway management and respiratory system, followed by cardiovascular events. There was a great variation in reporting the methods, particularly poor definitions of diagnostic criteria for complications. Data were heterogeneous and pooled estimates may not be generalizable. Some studies failed to define potential source of bias, explain how missing data were addressed, describe acute severe complications, and had incomplete postoperative follow-up. CONCLUSION: The data on pediatric anesthesia acute severe complications are poorly defined with large variation in the specificity of diagnostic reporting even within studies. We suggest that it is vital for future studies in this area to be based on a standardized system of diagnostic reporting (possibly with a hierarchical system of coding) with adequate description of population details to describe heterogeneity of data.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.021 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".