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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".