Emerging use of combination therapies for the management of type 2 diabetes – focus on saxagliptin and dapagliflozin
Bibliographic record
Abstract
AIMS: The aim of this article is to review the safety and efficacy data of dapagliflozin, saxagliptin, and their combination in the management of patients with type 2 diabetes. Evidence for the use of the single-tablet combination formulation is also presented. METHODS: A nonsystematic literature review was performed using the Ovid, PubMed, and Google Scholar databases. RESULTS: The addition of dapagliflozin/saxagliptin to metformin can lower mean hemoglobin A1c by as much as 1.47% and lead to weight loss of 0.5-2.0 kg. The risk of genital infections with combination therapy is lower than observed with dapagliflozin alone, suggestive of a protective effect. Adverse event risk at 52-week follow-up was not increased beyond that seen with either monotherapy. CONCLUSION: Dapagliflozin/saxagliptin combination is generally well tolerated and is an effective tool in helping patients with diabetes improve glycemic control.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".