Childhood Maltreatment in Patients Undergoing Bariatric Surgery: Implications for Weight Loss, Depression and Eating Behavior
Bibliographic record
Abstract
We aimed to explore the relationships between childhood maltreatment and changes in weight, depressive symptoms and eating behavior post-bariatric surgery (BS). Participants (n = 111, 85% females) were evaluated pre-surgery, and at 6 months (6 M) and 12 months (12 M) post-BS. History of maltreatment was assessed at baseline (Childhood Trauma Questionnaire), and depressive symptoms (Beck Depression Inventory-II) and eating behavior (Dutch Eating Behavior Questionnaire) were assessed at all time points. Participants’ mean age and median BMI were 45.1 ± 11.7 years and 46.7 (IQR 42.4–51.9) kg/m2, respectively. Histories of emotional (EA), physical (PA) and sexual abuse (SA) and emotional (EN) and physical (PN) neglect were reported by 47.7%, 25.2%, 39.6%, 51.4% and 40.5%, respectively, with 78.4% reporting at least one form of maltreatment. Changes in weight and depressive symptoms were not different between patients with vs. without a history of maltreatment. However, those with vs. without SA demonstrated limited changes in emotional eating (EE) at 12 M, while those without showed improvements. Conversely, patients with vs. without EN showed greater improvements in external eating (ExE) at 6 M, but differences were no longer observed by 12 M. Results indicate that histories of SA and EN are associated with changes in eating behaviors post-BS and have implications for assessment, monitoring and potential intervention development.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".