Association between residual islet beta‐cell function and achieving the target of time in range in inpatients with type 2 diabetes undergoing antidiabetic treatment: An observation study
Bibliographic record
Abstract
AIM: To assess whether the beta-cell function of inpatients undergoing antidiabetic treatment influences achieving time in range (TIR) and time above range (TAR) targets. MATERIALS AND METHODS: This cross-sectional study included 180 inpatients with type 2 diabetes. TIR and TAR were assessed by a continuous glucose monitoring system, with target achievement defined as TIR more than 70% and TAR less than 25%. Beta-cell function was assessed by the insulin secretion-sensitivity index-2 (ISSI2). RESULTS: Following antidiabetic treatment, logistic regression analysis showed that lower ISSI2 was associated with a decreased number of inpatients achieving TIR (OR = 3.10, 95% CI: 1.19-8.06) and TAR (OR = 3.40, 95% CI: 1.35-8.55) targets after adjusting for potential confounders. Similar associations still existed in those participants treated with insulin secretagogues (TIR: OR = 2.91, 95% CI: 0.90-9.36, P = .07; TAR, OR = 3.14, 95% CI: 1.01-9.80) or adequate insulin therapy (TIR: OR = 2.84, 95% CI: 0.91-8.81, P = .07; TAR, OR = 3.24, 95% CI: 1.08-9.67). Furthermore, receiver operating characteristic curves showed that the diagnostic value of the ISSI2 for achieving TIR and TAR targets was 0.73 (95% CI: 0.66-0.80) and 0.71 (95% CI: 0.63-0.79), respectively. CONCLUSIONS: Beta-cell function was associated with achieving TIR and TAR targets. Stimulating insulin secretion or exogenous insulin treatment could not overcome the disadvantage of lower beta-cell function on glycaemic 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".