Semaglutide 2.4 mg for the Treatment of Obesity: Key Elements of the STEP Trials 1 to 5
Bibliographic record
Abstract
OBJECTIVE: The obesity epidemic is a public health concern, warranting further research into pharmacological treatments for weight management (WM) as an adjunct to lifestyle interventions. The Semaglutide Treatment Effect in People with obesity (STEP) program aims to investigate the effect of semaglutide versus placebo on weight loss, safety, and tolerability in adults with obesity or overweight. METHODS: Across five phase 3 trials (NCT03548935, WM; NCT03552757, WM in type 2 diabetes; NCT03611582, WM with intensive behavioral therapy; NCT03548987, sustained WM; and NCT03693430, long-term WM), ~5,000 participants are being randomly assigned to receive semaglutide 2.4 mg once weekly subcutaneously versus placebo. Results will be available in 2020/2021. For all trials, the primary end point is change from baseline to end of treatment in body weight. RESULTS: and a mean waist circumference of 113.0 to 115.7 cm. CONCLUSIONS: The STEP program evaluates the efficacy and safety of semaglutide 2.4 mg subcutaneously once weekly in a broad population. The trials will provide insights on WM in people with obesity with and without type 2 diabetes and on long-term follow-up.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".