Predicting Perioperative Respiratory Adverse Events in Children With Sleep-Disordered Breathing
Bibliographic record
Abstract
BACKGROUND: No evidence currently exists to quantify the risk and incidence of perioperative respiratory adverse events (PRAEs) in children with sleep-disordered breathing (SDB) undergoing all procedures requiring general anesthesia. Our objective was to determine the incidence of PRAEs and the risk factors in children with polysomnography-confirmed SDB undergoing procedures requiring general anesthesia. METHODS: Retrospective review of all patients with polysomnography-confirmed SDB undergoing general anesthesia from January 2009 to December 2013. Demographic and perioperative outcome variables were compared between children who experienced PRAEs and those who did not. Generalized estimating equations were used to build a predictive model of PRAEs. RESULTS: In a cohort of 393 patients, 51 PRAEs occurred during 43 (5.6%) of 771 anesthesia encounters. Using generalized estimating equations, treatment with continuous positive airway pressure or bilevel positive airway pressure (odds ratio, 1.63; 95% confidence interval [CI], 1.05-2.54; P = .031), outpatient (odds ratio, 1.37; 95% CI, 1.03-1.91; P = .047), presence of severe obstructive sleep apnea (odds ratio, 1.63; 95% CI, 1.09-2.42; P = .016), use of preoperative oxygen (odds ratio 1.82; 95% CI, 1.11-2.97; P = .017), history of prematurity (odds ratio, 2.31; 95% CI, 1.33-4.01; P = .003), and intraoperative airway management with endotracheal intubation (odds ratio, 3.03; 95% CI, 1.79-5.14; P < .001) were associated with PRAEs. CONCLUSIONS: We propose the risk factors identified within this cohort of SDB patients could be incorporated into a preoperative risk assessment tool that might better to identify the risk of PRAE during general anesthesia. Further investigation and validation of this model could contribute to improved preoperative risk stratification, decision-making (postoperative admission and level of monitoring), and health care resource allocation.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".