Prevalence and Predictors of Self-Reported Sexual Abuse in Severely Obese Patients in a Population-Based Bariatric Program
Bibliographic record
Abstract
BACKGROUND: Sexual abuse may be associated with poorer weight loss outcomes following bariatric treatment. Identifying predictors of abuse would enable focused screening and may increase weight management success. METHODS: We analyzed data from 500 consecutively recruited obese subjects from a population-based, regional bariatric program. The prevalence of self-reported sexual abuse was ascertained using a single interview question. Health status was measured using a visual analogue scale (VAS). Multivariable logistic regression was performed to identify sexual abuse predictors. RESULTS: The mean age was 43.7 y (SD 9.6), 441 (88.2%) were females, 458 (91.8%) were white, and the mean body mass index (BMI) was 47.9 kg/m(2) (SD 8.1). The self-reported prevalence of past abuse was 21.8% (95% CI 18.4-25.4%). Abused subjects had worse health status (VAS score 53.1 (SD 21.2) versus 58.0 (SD 20.1), P = 0.03). BMI was not associated with abuse (P > 0.5). Age, sex, BMI, and covariate-adjusted independent predictors of abuse included alcohol addiction (adjusted odds ratio 15.8; 95% CI 4.0-62.8), posttraumatic stress disorder (4.9; 2.5-9.5), borderline personality (3.8; 1.0-13.8), depression (2.4; 1.3-4.3), and lower household income (3.4; 1.6-7.0). CONCLUSIONS: Abuse was common amongst obese patients managed in a population-based bariatric program; alcohol addiction, psychiatric comorbidities, and low-income status were highly associated with sexual abuse.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".