Effects of Tofogliflozin on Cardiac Function in Elderly Patients With Diabetes Mellitus
Bibliographic record
Abstract
Background: Patients with type 2 diabetes mellitus (T2DM) are at increased risk for impairments in diastolic relaxation and heart failure with preserved ejection fraction (EF). Recent clinical data suggest that several sodium glucose transporter-2 (SGLT2) inhibitors are found to reduce cardiovascular disease (CVD) events in elderly diabetic patients, but the effect of tofogliflozin, one of the SGLT2 inhibitors, on CVD is unknown. We retrospectively investigated the effect of tofogliflozin on cardiac function in elderly patients with T2DM. Methods: Patients received 20 mg of tofogliflozin daily for 1 month. EF, ratio of early filling to atrial filling (E/A), a change in mitral inflow E and mitral e' annular velocities (E/e'), left atrial dimension (LAD) and maximal diameter of inferior vena cava (IVC max ) were measured between baseline and 1 month after the administration of tofogliflozin. Results: Body weight, systolic and diastolic blood pressures significantly decreased, while renin and aldosterone level significantly increased after 1 month of tofogliflozin treatment. Most of the physiological parameters and the level of serum electrolyte did not change significantly. E/A, E/e' and LAD significantly decreased, while no significant changes were observed in EF and IVC max . The interactions of E/e' between time, gender and age were not significant. Conclusion: The present study suggested that tofogliflozin improved left ventricular diastolic function irrespective of gender and age, while preserving IVC, renal function and electrolyte balance. J Clin Med Res. 2020;12(3):165-171 doi: https://doi.org/10.14740/jocmr4098
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".