The Impact of Bariatric Surgery on Psychological Health
Bibliographic record
Abstract
Obesity is associated with a relatively high prevalence of psychopathological conditions, which may have a significant negative impact on the quality of life. Bariatric surgery is an effective intervention in the morbidly obese to achieve marked weight loss and improve physical comorbidities, yet its impact on psychological health has yet to be determined. A review of the literature identified a trend suggesting improvements in psychological health after bariatric surgery. Majority of mental health gain is likely attributed to weight loss and resultant gains in body image, self-esteem, and self-concept; however, other important factors contributing to postoperative mental health include a patient's sense of taking control of his/her life and support from health care staff. Preoperative psychological health also plays an important role. In addition, the literature suggests similar benefit in the obese pediatric population. However, not all patients report psychological benefits after bariatric surgery. Some patients continue to struggle with weight loss, maintenance and regain, and resulting body image dissatisfaction. Severe preoperative psychopathology and patient expectation that life will dramatically change after surgery can also negatively impact psychological health after surgery. The health care team must address these issues in the perioperative period to maximize mental health gains 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".