Characterization of the effect of naltrexone/bupropion on body composition
Bibliographic record
Abstract
AIMS: Oral treatment extended-release naltrexone/bupropion (NB) leads to significant weight loss, but its effect on body composition remains unclear. We investigated changes in body composition with dual-energy x-ray absorptiometry after treatment with NB or placebo in a subgroup of participants from a randomized control phase 3 study (COR-I). MATERIALS AND METHODS: Observed changes from baseline to week 52 were estimated for total, lean, and fat mass. Changes in body composition were evaluated using linear regression and adjusted for baseline covariates. RESULTS: The analysis included 82 participants (placebo, n = 26; NB, n = 56) with comparable baseline characteristics (age, BMI, sex). The NB group experienced a significant -7.8% change of total mass (-12.9% change in fat mass and -4.1% in lean mass), compared with a -2.8% change of total mass (-4.8% change in fat mass and -1.4% in lean mass) in the placebo group. The adjusted changes in lean-to-fat mass ratio of 0.069 in the NB group and -0.056 in the placebo group were significantly different (p < 0.05). CONCLUSIONS: NB-induced weight loss is associated with significant reductions in total percent fat mass, increase in total percent lean mass, and change in lean-to-fat mass ratio, in comparison to placebo. Larger studies are needed to further elucidate the clinical significance of these changes and impact of a potentially healthier metabolism.
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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 | 0.001 |
| 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) | 0.002 | 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".