Evolution of depressive symptoms from before to 24 months after bariatric surgery: A systematic review and meta‐analysis
Bibliographic record
Abstract
AIMS: Depression after bariatric surgery can lead to suboptimal health outcomes. However, it is unclear how depressive symptoms evolve over the 24 months after surgery. We determined the extent depressive symptoms changed up to 24 months after bariatric surgery and how this was impacted by measurement tool and surgical procedure. METHODS: We conducted a systematic review and meta-analysis, searching five databases from database inception to June 2021 for studies that prospectively measured depressive symptoms before and up to 24 months after bariatric surgery. Change scores were converted to Hedge's g, and analyses were performed using mixed-effects models. Subgroup analyses examined differences across time of follow-up, measurement tool, and surgical procedure. FINDINGS: = 95.7%). Subgroup analyses found that symptom reductions did not differ between the timing of follow-up periods, measurement tool, and surgical procedure. CONCLUSIONS: Depressive symptom scores reduced substantially following surgery; comparable decreases occurred 6 through 24 months after surgery. These findings can help inform practitioners of the typical evolution of depressive symptoms following surgery and where deviations from this may require additional intervention.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.022 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".