The Bariatric Interprofessional Psychosocial Assessment of Suitability Scale predicts binge eating, quality of life and weight regain following bariatric surgery
Bibliographic record
Abstract
Presurgical psychosocial evaluations are an important component of bariatric care; yet, bariatric programs vary widely in their assessment and interpretation of psychosocial risk. There is a need for validated clinical tools that help to standardize and streamline the assessment of variables relevant to surgical outcomes. The present study contributes to the validation of the Bariatric Interprofessional Psychosocial Assessment of Suitability Scale (BIPASS), a novel presurgical psychosocial evaluation tool, by: (a) examining the psychometric properties and optimal cutoff score, and; (b) examining the ability of the BIPASS tool to predict outcomes 1 and 2 years postsurgery, including weight regain, quality of life, psychiatric symptoms and adherence to postsurgical follow-up appointments. The BIPASS was applied retrospectively to the charts of 179 consecutively referred patients to a metropolitan bariatric surgery programme. Internal consistency for the BIPASS was acceptable, and interrater reliability was excellent. Higher BIPASS scores predicted higher binge eating symptomatology and lower mental health-related quality of life at 1 year postsurgery, and weight regain at 2 years (all P < .01). The BIPASS did not predict adherence to postsurgical follow-up appointments. Findings suggest that the BIPASS can be used to identify patients at increased risk of disordered eating, poor quality of life and weight regain early in the postsurgical course, thereby facilitating patient education and appropriate interventions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".