Variations in weight loss and glycemic outcomes after sleeve gastrectomy by race and ethnicity
Bibliographic record
Abstract
OBJECTIVE: This study examined racial and ethnic differences in percent total weight loss (%TWL) and glycemic improvement following sleeve gastrectomy (SG) and explored the role of socioeconomic and psychosocial factors in postsurgical outcomes. METHODS: This longitudinal study included patients who underwent SG between 2017 and 2020, with follow-up visits over 24 months. RESULTS: Non-Hispanic Black (NHB) participants had lower %TWL at 3, 12, and 24 months compared with Hispanic (H) and non-Hispanic White (NHW) participants. Fat mass index was initially lower in NHB, with smaller reductions over time and significant group differences persisting at 24 months. NHB participants had higher baseline fat-free mass index values; by 24 months, fat-free mass index values were lower in H participants. Hemoglobin A1c decreased across all groups but remained consistently higher in NHB and H compared with NHW at 24 months. NHB participants reported higher perceived discrimination, sleep disturbance, and perceived stress than H and NHW participants at all time points. Employment status predicted %TWL at 12 months. There was a significant interaction between race and ethnicity and employment status observed at 12 and 24 months, suggesting that employment-related disparities could impact surgical outcomes. CONCLUSIONS: NHB participants experienced less favorable outcomes following SG, emphasizing the need for tailored interventions addressing socioeconomic and psychosocial disparities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".