Comparison of major complications in children after laparoscopy-assisted gastrostomy and percutaneous endoscopic gastrostomy placement: a meta-analysis
Bibliographic record
Abstract
PURPOSE: A meta-analysis was performed to compare the rates of the major complications associated with two gastrostomy tube placement techniques in a pediatric population: laparoscopy-assisted gastrostomy (LAG) and percutaneous endoscopic gastrostomy (PEG). METHODS: The PubMed electronic database was queried for comparative studies of the two insertion techniques. The Newcastle-Ottawa scale (NOS) was used for the assessment of the quality and risk of bias in the included studies. The main outcome measure was the frequency of major complications defined as the need for reoperation within 30 days or death. RevMan 5.3, was used, with a p < 0.05 indicating statistical significance. RESULTS: Eight studies including 1550 patients met the inclusion criteria. The risk for major complications was higher in PEG than in LAG 3.86 (95% confidence interval 1.90-7.81; p < 0.0002). The number needed to treat to reduce one major complication by performing LAG instead of PEG was 23. There were no randomized-controlled trials. Overall, the quality of the included studies was determined to be unsatisfactory. CONCLUSIONS: PEG placement was associated with a significantly higher risk of major complications compared to LAG placement. Therefore, LAG should be the preferred method for gastrostomy tube placement 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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.040 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".