The Bariatric Interprofessional Psychosocial Assessment of Suitability Scale (BIPASS): Predictive Validity for Outcomes 1 and 2 Years Following Bariatric Surgery
Bibliographic record
Abstract
Bariatric surgery is the most effective intervention for severe obesity; however, many patients demonstrate insufficient and/or unsustained weight loss, and unsatisfactory psychosocial functioning in the longer-term. Although it is well established that attendance at postsurgical follow-up appointments is integral to sustained weight loss, nonadherence to follow-up is common. Consequently, presurgical psychosocial evaluations are conducted in order to identify patients at high risk of poor outcomes. Yet, no consensus has been established regarding a standardized protocol for the assessment of variables relevant to surgical outcomes, and bariatric programs vary widely in their interpretation of psychosocial risk. In addition, there is a paucity of research examining the predictive utility of psychosocial evaluations. The Bariatric Interprofessional Psychosocial Assessment of Suitability Scale (BIPASSTM), a novel psychosocial evaluation tool, was developed to address these issues. The purpose of the present study was to contribute to the validation of the BIPASS tool via two aims: 1) by examining the psychometric properties of the BIPASS, and; 2) by examining the ability of the BIPASS tool to predict outcomes 1 and 2 years following bariatric surgery, including weight loss and weight regain, quality of life, psychiatric symptoms, and adherence to postsurgical follow-up appointments. The BIPASS was applied retrospectively to the charts of 200 consecutively referred patients of the Toronto Western Hospital Bariatric Surgery Program (TWH-BSP). Factor analysis of BIPASS items revealed a two-factor structure, reflecting “Mental Health” and “Patient Readiness” subscales. Internal consistency for the BIPASS Total and subscale scores ranged from poor to good, and inter-rater reliability was excellent. Higher BIPASS scores significantly predicted higher binge eating symptomatology, and lower physical and mental health-related quality of life at 1 year postsurgery. The BIPASS did not predict any outcome variables at 2 years postsurgery, or adherence to postsurgical follow-up appointments. Findings suggest that the BIPASS can be used to identify patients at increased risk of problematic eating and poor health-related quality of life early in the postsurgical course, thereby facilitating 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 | 0.001 |
| 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".