Efficacy and Safety of Sitagliptin in Japanese Patients With Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to investigate the clinical efficacy and safety of sitagliptin in Japanese patients with type 2 diabetes. METHODS: A total of 3,247 subjects treated with sitagliptin were retrospectively recruited. Glucose parameters were collected at baseline, and 1, 3 and 6 months after initiation of sitagliptin. In addition, we explored factors that can be used to predict sitagliptin-induced reduction in HbA1c using linear mixed effect model. Factors associated with hypoglycemic events were examined by logistic analyses. RESULTS: We analyzed the available data of 3,201 subjects (1,287 females). Treatment of sitagliptin significantly reduced HbA1c level from 7.44±1.20% at baseline to 6.73±0.99% at 6 months (P < 0.0001). Linear mixed effect model analyses demonstrated that reduction of HbA1c was associated with higher baseline HbA1c level, younger age, lower BMI and sitagliptin monotherapy. During this study, 82 cases of hypoglycemia were recorded. Logistic analyses indicated that hypoglycemic events were more frequent in female patients, and patients with low BMI, long history of type 2 diabetes, high HbA1c and on combination therapy experienced. Other adverse events were rare and mild. CONCLUSIONS: Sitagliptin is effective for diabetic management and generally well tolerated in Japanese patients with type 2 diabetes. This trial was registered with UMIN (no. 000004121).
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".