A Study of Seasonal Variation in the Effect of Add-On Sitagliptin on Blood Glucose Control in Insulin-Treated Patients With Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: There are several reports of seasonal variation in hemoglobin A1c (HbA1c) in patients with type 2 diabetes (T2DM), but no reports of seasonal variation in the effect of add-on drugs on blood glucose control in insulin-treated patients. METHODS: Using data collected from 630 patients in a multicenter study, we compared the amount of change in HbA1c after 1, 3, 6, 9, and 12 months of add-on administration of sitagliptin in insulin-treated patients divided into four groups based on the month when sitagliptin was started. RESULTS: A significantly larger decrease in HbA1c at 6 months from baseline was observed in the group that started add-on sitagliptin in February to April than in the other three groups. However, the amount of change in HbA1c at 12 months did not differ among the groups. CONCLUSIONS: The consideration of seasonal variation enables more accurate evaluation of a drug's short-term effect on blood glucose 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".