Mental health quality of life after bariatric surgery: A systematic review and meta‐analysis of randomized clinical trials
Bibliographic record
Abstract
Recent literature has raised concerns regarding the risk of adverse psychiatric events among bariatric surgery patients. However, the relationship between weight loss therapy and psychiatric outcomes is confounded by baseline psychosocial characteristics in observational studies. To understand the impact of bariatric surgery on the risk of adverse mental health outcomes, we conducted a systematic review and meta-analysis of randomized controlled trials that compared surgical and non-surgical treatments and assessed mental health quality of life (QoL). We evaluated the PubMed, EMBASE, Web of Science PsycINFO, Clinicaltrials.gov and Cochrane databases through 7 March 2018. Pooled standardized mean differences (SMDs) for mental health QoL scores were estimated using random effects models. Eleven randomized trials with 731 participants were included in the final analyses. Surgery was not associated with an improvement in mental health QoL from baseline as compared to non-surgical intervention (SMD: 0.02, 95% confidence interval [CI] -0.22 to 0.25). Final mental health QoL scores were similar for surgically and non-surgically treated patients (SMD: 0.37, 95% CI -0.07 to 0.81). Subgroup analyses assessing the effect of specific surgical interventions, and varying lengths of follow-up did not identify a beneficial effect of bariatric surgery on mental health QoL outcomes. These results, in conjunction with the fact that individuals who choose bariatric surgery tend to have high-risk baseline characteristics, suggest that intensive mental health follow-up following surgery should be routinely considered.
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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 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".