Comparative Efficacy of Tirzepatide vs. Semaglutide in Reducing Body Weight in Humans: A Systematic Review and Meta-Analysis of Clinical Trials and Real-World Data
Bibliographic record
Abstract
Background: The aim of the study was to compare the effectiveness of tirzepatide versus semaglutide in producing weight loss. Methods: tests. A random-effect model was used to calculate pooled "mean differences" (MDs). Study quality was estimated by Newcastle-Ottawa Quality Assessment Scale (NOS) and Cochrane risk of bias (RoB) version 2 tool, and publication bias was estimated through forest plots and the Egger's test. Results: A total of two randomized controlled trials (RCTs) and five retrospective cohorts were included in this MA. MA results showed that compared with the semaglutide, tirzepatide could produce significantly greater weight loss (MD = 4.23; 95% confidence interval (CI): 3.22 - 5.25; P < 0.01). Subgroup analysis showed a dose- and duration-dependent significantly superior therapeutic effect of tirzepatide (> 10 mg dose: MD = 6.50, 95% CI: 5.93 - 7.08, P < 0.01 vs. ≤ 10 mg: MD = 3.89, 95% CI: 2.12 - 5.65, P < 0.01) (> 6 months duration: MD = 5.00, 95% CI: 3.48 - 6.52, P < 0.01 vs. ≤ 6 months: MD = 3.50, 95% CI: 2.24 - 4.75, P < 0.01). The supremacy of tirzepatide was maintained in both types of studies: RCTs and retrospective cohorts. No publication bias was found through forest plots visually or Egger's test (Egger's regression asymmetry test P value 0.94). Study quality estimated by NOS revealed the quality of each study as "good" (≥ 7 points) and that estimated by the Cochrane RoB tool revealed "low" RoB. Conclusion: The pooled analysis provides evidence that tirzepatide is better than semaglutide in reducing body weight, regardless of study design. A dose-response relationship exists, and the weight loss magnitude increases with the dose or duration of tirzepatide. The studies that provide this evidence are of high quality and have a low RoB.
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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 | 0.028 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.036 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".