Long‐term health‐related quality of life in bariatric surgery patients: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: Bariatric surgery results in significant weight loss in the majority of patients. Improvement in health-related quality of life (HRQoL) is an equally important outcome; however, there are few studies reporting long-term (≥5 years) HRQoL outcomes. This study assesses the quality of evidence and effectiveness of surgery on HRQoL ≥ 5 years. METHODS: PubMed, Cochrane Review, EmBase, CINANL, PsycInfo, obesity conference abstracts, and reference lists were searched. Keywords were bariatric surgery, obesity, and quality of life. Studies were included if (1) there was ≥5 years follow-up, (2) patients had class II or III obesity, (3) individuals completed a validated HRQoL survey, and (4) there was a nonsurgical comparison group with obesity. Two reviewers independently assessed each study. RESULTS: From 1376 articles, 9 studies were included in the systematic review (SR) and 6 in the meta-analysis (MA). Inconsistent results for long-term improvements in physical and mental health emerged from the SR. In contrast, the MA found significant improvements in these domains ≥5 years after surgery. CONCLUSIONS: Study findings provide evidence for a substantial and significant improvement in physical and mental health favoring the surgical group compared with controls spanning 5 to 25 years after surgery.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.024 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".