Efficacy of tirzepatide 5, 10 and 15 mg versus semaglutide 2 mg in patients with type 2 diabetes: An adjusted indirect treatment comparison
Bibliographic record
Abstract
AIM: To conduct an adjusted indirect treatment comparison (aITC) of the efficacy of tirzepatide 5/10/15 mg versus semaglutide 2 mg in patients with type 2 diabetes. MATERIALS AND METHODS: The primary analysis was a Bucher aITC of the change from baseline at week 40 in HbA1c (%) and body weight (kg). Aggregate data from the SURPASS-2 study that met the HbA1c inclusion criterion of the SUSTAIN FORTE study and from SUSTAIN FORTE metformin-only treated patients were used for primary analysis. RESULTS: The SURPASS-2 refined population comprised 238/245/240 and 240 participants for tirzepatide 5/10/15 mg and semaglutide 1 mg, respectively. The SUSTAIN FORTE metformin-only population comprised 222 and 227 participants for semaglutide 1 and 2 mg, respectively. In this aITC, tirzepatide 10 and 15 mg significantly reduced HbA1c versus semaglutide 2 mg with an estimated treatment difference (ETD) of -0.36% (95% confidence interval [CI] -0.63, -0.09) and -0.4% (95% CI -0.67, -0.13), respectively. Tirzepatide 10 and 15 mg significantly reduced body weight versus semaglutide 2 mg with an ETD of -3.15 kg (95% CI -4.84, -1.46) and -5.15 kg (95% CI -6.85, -3.45), respectively. There were no significant differences between tirzepatide 5 mg and semaglutide 2 mg on change from baseline in HbA1c and body weight. CONCLUSIONS: In this aITC, HbA1c and weight reductions were significantly greater for tirzepatide 10 and 15 mg versus semaglutide 2 mg and were similar for tirzepatide 5 mg versus semaglutide 2 mg. These findings provide comparative effectiveness insights in the absence of a head-to-head clinical trial.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".