The response to short‐term intensive insulin therapy in type 2 diabetes
Bibliographic record
Abstract
AIM: Although a short course of intensive insulin therapy (IIT) can improve beta-cell function and glycaemic control in most patients with newly diagnosed type 2 diabetes (T2DM), the impact of this intervention in diabetes of longer duration has not been carefully studied. Thus, we sought to evaluate the effect of short-term IIT in patients with established T2DM. METHODS: Thirty-four patients, with diabetes of mean 5.9 +/- 6.6 years duration, underwent 4-8 weeks of IIT, with 4-h meal test administered at baseline and at 1 day post-IIT. A positive clinical response was defined as fasting glucose < 7.0 mmol/l off any antidiabetic therapy at the latter test. RESULTS: A positive response was achieved in 68% (n = 23) of the subjects. At baseline meal test, the responders had lower glucose levels than the non-responders from 120 to 240 min (all timepoints p < or = 0.0008) and higher late incremental area-under-the-C-peptide-curve (AUC(Cpep)), particularly from 60 to 150 min (all p < 0.005). Beta-cell function (ratio of AUC(Cpep) to AUC(gluc) divided by HOMA-IR) was similar between the groups at baseline (median 54.1 vs. 51.3, p = 0.62) but after IIT was significantly higher in the responders (109.3 vs. 57.4, p = 0.009). At baseline, the strongest predictors of the change in beta-cell function were glucose levels between 180 and 240 min (all r = -0.5, p = 0.005) and incremental AUC(Cpep) from 120 to 180 min (all r > or = 0.66, p < or = 0.0001), both reflecting late-phase insulin secretion. CONCLUSIONS: The clinical response to short-term IIT is variable, consistent with the heterogeneity of T2DM. However, preserved late-phase insulin secretion may identify those patients who can benefit from this intervention with improved 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| 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".