Evaluating remission of type 2 diabetes using a metabolic intervention including fixed‐ratio insulin degludec and liraglutide: A randomized controlled trial
Bibliographic record
Abstract
AIM: To evaluate the effect on type 2 diabetes remission of short-term intensive metabolic intervention consisting of frequent dietary, exercise and diabetes management coaching, metformin and fixed-ratio insulin degludec/liraglutide. METHODS: In a multicentre open-label randomized controlled trial, insulin-naïve participants within 5 years of diabetes diagnosis were assigned to a 16-week remission intervention regimen or standard care, and followed for relapse of diabetes and sustained remission for an additional year after stopping glucose-lowering drugs. RESULTS: , and glycated haemoglobin (HbA1c) level 53 ± 7 mmol/mol were randomized and analysed (79 intervention, 80 control). At the end of the 16-week intervention period, compared to controls, intervention participants achieved lower HbA1c levels (40 ± 4 vs. 51 ± 7 mmol/mol; p < 0.0001), and lost more weight (3.3 ± 4.4% vs. 1.9 ± 3.0%; p = 0.02). There was a lower hazard of diabetes relapse overall in the intervention group compared to controls (hazard ratio 0.63, 95% confidence interval [CI] 0.45, 0.88; p = 0.007), although this was not sustained over time. Remission rates in the intervention group were not significantly higher than in the control group at 12 weeks (17.7% vs. 12.5%, relative risk [RR] 1.42, 95% CI 0.67, 3.00; p = 0.36) or at 52 weeks (6.3% vs. 3.8%, RR 1.69, 95% CI 0.42, 6.82) following the intervention period. CONCLUSIONS: An intensive remission-induction intervention including fixed-ratio insulin degludec/liraglutide reduced the risk of type 2 diabetes relapse within 1 year without sustained remission.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".