Postprandial hyperglycaemia following insulin suspensions by the artificial pancreas: Implications for bolus calculators
Bibliographic record
Abstract
Conventional bolus calculators apply negative prandial corrections when premeal glucose levels are low. However, no study has evaluated the need for this negative correction with closed-loop systems. We analysed data retrospectively from a cohort study evaluating a closed-loop artificial pancreas system conducted in a diabetes camp over a period of 11 days. Meal boluses with negative correction (n = 98) of 47 participants aged 8 to 22 years were examined. If there was no insulin-on-board from previous boluses at mealtime, the postprandial hyperglycaemia rate increased with increased duration of insulin suspension (P = .03), with odds ratios being exaggerated by 17% per 10 minutes of suspension. However, if there was insulin-on-board from previous boluses, the hyperglycaemia rate did not change with increased duration of insulin suspension (P = .24). When there was no insulin-on-board, the rate of hyperglycaemia after meals preceded by no suspension was 21% (3/14), compared with 52% (12/23) and 64% (9/14) after meals preceded by suspensions of ≥50 and ≥70 minutes, respectively. Meal size did not influence these results. We conclude that, in the absence of insulin-on-board, negative prandial corrections may not be necessary following long insulin suspensions.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".