Real-World Evidence Study of Naltrexone/Bupropion for Achieving Clinically Significant Weight Loss
Bibliographic record
Abstract
Background: Naltrexone/Bupropion (NB) was approved as an anti-obesity medication (AOM) based on the Contrave Obesity Research (COR) trials, which assessed weight loss at weeks 28 and 56. Prior research suggests that weight loss within three months of AOM treatment predicts long-term success. This study evaluates the real-world effectiveness of NB in achieving clinically significant weight loss after three months of adherence and compares long-term outcomes to the COR trials. Methods: A retrospective chart review was conducted at a Canadian community clinic. Patients were included if they had obesity (class I–III), adhered to NB for at least three months, were treated solely with NB (June 2019–March 2023), and were ≥18 years old. Weight loss was calculated from baseline at three months, and for those with longer adherence (26, 52, and 104 weeks). Results: Among 125 patients completing three months of NB, the mean weight loss was 4.9% (clinically significant). Of those continuing NB, 57.6% had already lost ≥5% by three months. At six months (n=72), weight loss was 8.6%. At 52 weeks (n=33), it was 11.3%, and at 104 weeks (n=11), it reached 11.8%. Conclusion: NB effectively achieves clinically significant weight loss within three months. Longer-term real-world outcomes align with COR trial findings, supporting NB’s sustained effectiveness in obesity treatment.
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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 | 0.044 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".