28. Effect of Ertugliflozin on Blood Pressure in Patient with Type 2 Diabetes Mellitus: a Systematic Review
Bibliographic record
Abstract
Type 2 Diabetes Mellitus (T2DM) is a common worldwide disease caused by a sedentary lifestyle. Hypertension is one of the most common comorbid in T2DM. One of the new anti-diabetic classes is Sodium-Glucose Cotransporter-2 (SGLT2) inhibitor which is still widely tested in clinical trials for indications beyond T2DM. However, the effect of Ertugliflozin on the blood pressure of T2DM patients is still not well known. This study wants to find out the effect of Ertugliflozin on blood pressure in patients with T2DM. This study was conducted on 1–7 January 2023. Two independent researchers systematically extracted data from several databases, such as PubMed Central (PMC), Science Direct, and PUBMED by using MeSH terminology of keywords SGLT2 inhibitor and blood pressure. The extracted studies were then analyzed and selected according to our inclusion criteria such as studies in the last 5 years, T2DM patients receiving ertugliflozin, cohort studies, and case-control studies. We excluded systematic reviews, meta-analyses, case series, case reports, studies on pregnant women, children, and animals. Research quality was assessed using Newcastle-Ottawa (NOS). From 5 cohort studies (5580 subjects from various countries), all of them showed that ertugliflozin improved systolic blood pressure in T2DM patients. The results were similar either in studies using ertugliflozin alone or in combination with other classes of anti-diabetic drugs; All studies have proven good quality based on NOS. In conclusion, ertugliflozin had improved the blood pressure in T2DM patients. However, further study is needed to confirm these findings.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".