Ethnic differences in weight loss during a clinical obesity management program
Bibliographic record
Abstract
Summary To examine ethnic differences in how individuals respond to obesity management therapies, a retrospective chart review of the Wharton Medical Weight Management Clinic electronic medical records was used (n = 21 709; 14 695 patients with weight loss data). South and East Asian, Middle Eastern and Other ethnicities had a significantly lower body mass index (BMI) at enrollment than White adults (39.7 vs. 35.4–38.7 kg/m2), with higher or similar BMIs in Indigenous and Black adults (39.9–42.2 kg/m2). Whites, East Asians and Other Ethnicities had the greatest weight loss (4.3–4.9 kg), while Blacks (3.3 kg), Latin (3.0 kg), Middle Eastern (2.7 kg), and South Asians (3.5 kg) lost significantly less weight as compared to Whites (4.9 kg) (p < .05). There were also weight loss differences between Black sub‐groups. African American females lost the least weight (1.4 kg), while West Indian Black females lost much more weight (4.3 kg, p = .01). African American males also lost the least amount of weight (0.9 kg), while African Black males lost the most (7.4 kg, p = 0.01). There are differences in the weight loss achieved during a clinical obesity management program between individuals of various ethnicities.
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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 | 0.000 | 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) | 0.001 | 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".