Association of bariatric surgery with indicated and unintended outcomes: An umbrella review and meta‐analysis for risk–benefit assessment
Bibliographic record
Abstract
Bariatric surgery can cause numerous functional changes to recipients, some of which are unintended. However, a systematic evaluation of wide-angled health benefits and risks following bariatric surgery has not been conducted. We systematically evaluated published systematic reviews of randomized controlled trials and observational studies reporting the association between bariatric surgery and health outcomes. We performed subgroup analyses by surgery type and sensitivity analysis, excluding gastric band. Thirty systematic reviews and 82 meta-analyzed health outcomes were included in this review. A total of 66 (80%) health outcomes were significantly associated with bariatric surgery, of which 10 were adverse outcomes, including suicide, fracture, gastroesophageal reflux after sleeve gastrectomy, and neonatal morbidities. The other 56 outcomes were health benefits including new-onset diabetes mellitus (DM) (odds ratio [OR] = 0.39; 95% confidence interval [CI] = 0.19-0.79), hypertension (OR = 0.36; 95% CI = 0.33-0.40), dyslipidemia (OR = 0.33; 95% CI = 0.14-0.81), cancers (OR = 0.65; 95% CI = 0.53-0.80), cardiovascular diseases (CVDs), and women's health. Surgery is associated with reductions in all-cause mortality and death due to cancer, DM, and CVD. Bariatric surgery has both beneficial and harmful effects on a broader than expected array of patients' health outcomes. An expansion of the indication for bariatric surgery could be discussed to include a broader population with metabolic vulnerabilities.
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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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.046 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".