Effect of Obesity on Surgical Outcomes and Complication Rates in Pediatric Patients: A Comprehensive Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Obesity is one of the primary public health problems faced by children. The increased incidence of obesity in the pediatric population poses significant challenges during and after surgical procedures. This systematic review and meta-analysis aimed to understand to what extent obesity is to surgical complications in pediatric patients. A systematic database search of PubMed, Web of Science, Scopus, and Science Direct was performed in June 2023. According to the inclusion and exclusion criteria, two evaluators independently conducted literature screening, data extraction, and quality evaluation of the retrieved literature. The Newcastle-Ottawa Scale score was used for quality evaluation, and a meta-analysis was performed using Review Manager software 5.4.1. A total of 1,170 relevant articles were initially identified, and 20 articles were finally included for data extraction and meta-analysis. The results of the meta-analysis showed that compared with normal-weight individuals, obese pediatric patients had a higher risk of developing surgical site infection (SSI) (relative risk (RR) = 1.63; 95% confidence interval (CI) = 1.33-2.00), wound dehiscence (RR = 2.01; 95% CI = 1.24-3.23), and underwent procedures that were 11.32 minutes longer (95% CI = 5.36-17.29). There were no differences in bleeding requiring transfusion, deep venous thromboembolism, postoperative abscess rate, and length of stay. Obese pediatric patients have a higher risk of SSI and dehiscence, along with a longer operative time. The established risks in obese adults undergoing surgery suggest a similar risk for obese pediatric patients. The findings of this study hold significant implications for clinical practice, suggesting the potential for additional measures to prevent surgical complications in children.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.037 |
| Bibliometrics | 0.007 | 0.007 |
| 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) | 0.003 | 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".