Self-reported Causes of Weight Gain: Among Prebariatric Surgery Patients
Bibliographic record
Abstract
PURPOSE: Bariatric surgery is accepted by the medical community as the most effective treatment for obesity; however, weight regain after surgery remains common. Long-term weight loss and weight maintenance may be aided when dietitians who provide perioperative care understand the causes of weight gain leading to bariatric surgery. In this study, the most common causes for weight gain were examined among prebariatric surgery patients. METHODS: A retrospective chart review was conducted for 160 patients enrolled in a bariatric surgery program. Data were collected for 20 variables: puberty, pregnancy, menopause, change in living environment, change in job/career, financial problems, quitting smoking, drug or alcohol use, medical condition, surgery, injury affecting mobility, chronic pain, dieting, others' influence over diet, abuse, mental health condition, stress, death of a loved one, divorce/end of a relationship, and other causes. Frequency distribution and chi-square tests were performed using SPSS. RESULTS: Sixty-three percent of participants selected stress as a cause of weight gain, while 56% selected dieting. Significant differences existed between women and men in the selection of dieting and change in living environment. CONCLUSIONS: This information may allow dietitians to better identify causes for weight gain leading to bariatric surgery, and to address these causes appropriately before and 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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".