Baseline nocturnal glucose change: A predictor of the treatment effect of bolus intensification in insulin‐treated type 2 diabetes
Bibliographic record
Abstract
This post hoc analysis of an 18-week randomized trial explored the utility of calculating baseline glycated haemoglobin (HbA1c), postprandial glucose (PPG) increments and nocturnal glucose change in predicting efficacy and safety outcomes in response to bolus insulin intensification in people with type 2 diabetes (T2D). Analyses were conducted on 236 participants with T2D receiving metformin: 116 received fast-acting insulin aspart (faster aspart) basal-bolus therapy and 120 received basal-only insulin. Participants were grouped according to baseline HbA1c, PPG increments and nocturnal glucose change variables; analyses were performed on the end-of-trial treatment differences between "high" and "low" baseline values. The change from baseline in end-of-trial mean HbA1c and mean PPG increments was in favour of faster aspart across all subgroups. Significantly greater treatment differences were observed in participants with high (vs. low) baseline nocturnal glucose change and PPG increments. For baseline HbA1c, significantly greater treatment differences were observed for change in end-of-trial PPG increments, but not end-of-trial HbA1c. In conclusion, both nocturnal glucose change and PPG increments may be more useful than HbA1c for identifying subgroups of people with T2D who would most benefit from bolus intensification.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".