Effectiveness of surgical interventions for managing obesity in children and adolescents: A systematic review and meta‐analysis framed using minimal important difference estimates based on <scp>GRADE</scp> guidance to inform a clinical practice guideline
Bibliographic record
Abstract
OBJECTIVE: To summarize the literature on bariatric surgery for managing pediatric obesity, including intervention effects to improve patient-reported outcome measures (PROMs), cardiometabolic risk factors, anthropometry, and assess adverse events (AEs). METHODS: Eligible studies were published between January 2012 and January 2022 and included randomized controlled trials (RCTs) and observational (controlled and uncontrolled) studies before and after surgery with a mean age <18 years old. Outcomes and subgroups were selected a priori by stakeholders; estimates of effect for outcomes were presented relative to minimal important differences (MIDs) and GRADE certainty of evidence. We examined data on PROMs, cardiometabolic risk factors, anthropometry, and AEs. Subgroup analyses examined outcomes by follow-up duration and surgical technique, when possible. RESULTS: Overall, 63 publications (43 original studies) met our inclusion criteria (n = 6128 participants; 66% female). Studies reported six different surgical techniques that were evaluated using uncontrolled single arm observational (n = 49), controlled observational (n = 13), and RCT (n = 1) designs. Most studies included short-term follow-up (<18 months) only. PROMs were measured in 12 (28%) studies. Surgery led to large improvements in health-related quality of life compared to baseline and control groups, and moderate to very large improvements in cardiometabolic risk factors compared to baseline. Large to very large improvements in BMIz were noted compared to baseline across all follow-up periods. There was limited evidence of AEs with most reporting mild or non-specific AEs; serious AEs were uncommon. CONCLUSION: Bariatric surgery demonstrated primarily moderate to very large improvements across diverse outcomes with limited evidence of AEs, albeit with low to moderate certainty of evidence.
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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 | 0.043 | 0.099 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.041 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".