Who gains the most quality-of-life benefits from metabolic and bariatric surgery: findings from the prospective REBORN cohort study
Bibliographic record
Abstract
BACKGROUND: Prioritizing patients for metabolic and bariatric surgery (MBS) based on their potential postoperative benefits is essential. OBJECTIVES: To examine changes in quality of life (QoL) during the initial postoperative year among patients with diverse eligibility statuses and determine which group experiences greater benefits. SETTING: Center intégré universitaire de santé et de services sociaux du Nord-de-l'Île-de- Montréal (CIUSSS-NIM), Canada. METHODS: We categorized patients into 3 groups based on obesity class and the presence of comorbidities: Group 1 (obesity class II without comorbidities, n = 28); Group 2 (obesity class II with comorbidities, n = 36); and Group 3 (obesity class III, n = 460). QoL (Short-Form QoL questionnaire [SF-12]) and anthropometrics were measured at 6 months before, and 6 and 12 months after surgery. RESULTS: Repeated measures mixed models revealed a significant main effect of time (P < .001) and an interaction between time and group for the physical component of QoL (P = .007). These indicated consistent improvements across time in all groups, with the greatest benefits seen in Group 3 relative to Group 1. There were no interactions between time and group for the mental components of QoL (P = .402). There were significant interaction effects for weight and BMI (P's < .001), with Group 3 losing more weight than Groups 1 or 2. CONCLUSIONS: All groups that underwent MBS had improvements in the physical aspects of QoL and weight over time, even those who have traditionally not be considered eligible for MBS (i.e., Group 1). This provides a starting point to explore the importance of not excluding patients due to their weight and comorbidity status and setting comprehensive eligibility criteria encompassing all patients who might benefit from MBS, beyond just weight loss.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".