Short‐term intensive insulin as induction and maintenance therapy for the preservation of beta‐cell function in early type 2 diabetes ( <scp>RESET‐IT Main</scp> ): A 2‐year randomized controlled trial
Bibliographic record
Abstract
AIM: To test the hypothesis that the addition of periodic courses of short-term intensive insulin therapy (IIT) could enhance the effect of metformin (MET) maintenance therapy on preservation of beta-cell function following induction IIT. METHODS: In this multicentre, randomized controlled trial, 108 adults with type 2 diabetes (median 1.3 years' duration; HbA1c 6.6% ± 0.6%) were randomized to 3 weeks of induction IIT (glargine, lispro) followed by MET maintenance, either with or without periodic 2-week courses of IIT every 3 months for 2 years. Beta-cell function was assessed by the Insulin Secretion Sensitivity Index-2 (ISSI-2) at an oral glucose tolerance test every 3 months. RESULTS: In both arms, induction IIT increased ISSI-2, improved whole-body insulin sensitivity and reduced hepatic insulin resistance (all P ≤ .0004). The primary outcome of baseline-adjusted ISSI-2 at 2 years was not improved by the addition of intermittent IIT (MET + IIT) and was slightly higher in the MET arm (baseline-adjusted difference -35 [95% CI: -66, -3]), with three additional beta-cell measures showing no significant differences. Baseline-adjusted HbA1c at 2 years did not differ between MET and MET + IIT (6.3% ± 0.1% vs. 6.4% ± 0.1%, P = .46), with 32.6% of participants in each arm maintaining HbA1c of 6.0% or less at 2 years. CONCLUSION: Although initial induction IIT induces metabolic improvement, subsequent repeat courses of IIT every 3 months do not further enhance the effect of MET maintenance therapy on beta-cell function.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".