981-P: Postprandial Hyperglycemia following Insulin Suspensions by the Artificial Pancreas: Implications for Bolus Calculators
Bibliographic record
Abstract
Objective: Conventional bolus calculators apply negative prandial insulin corrections when pre-meal glucose levels are low. The present study examined the need for this negative correction with the artificial pancreas (AP). Methods: Data were retrospectively analysed from a study conducted in a camp for children with type 1 diabetes. Meal boluses with negative corrections (n=98) of 47 participants (between 8-22 years old) using the McGill AP for 11 days were examined. The duration of insulin suspension before each meal and the insulin-on-board (IOB) at mealtime were calculated. The effects of these factors on postprandial hyperglycemia were analysed. Results: The rate of postprandial hyperglycemia was 28% (18/65) for meals preceded by basal insulin suspensions <70 minutes, and 52% (17/33) for meals preceded by suspensions ≥70 minutes (p=0.02). The rate of postprandial hyperglycemia following suspensions ≥70 minutes compared to <70 minutes was significantly higher only if there was no IOB (64% [9/14] vs. 29% [13/45]; p=0.01). Meal size and total daily insulin dose did not influence these results. Conclusion: Negative corrections with meal boluses may not be necessary following long insulin suspensions in the absence of IOB. Disclosure S. Major: None. A. El Fathi: None. E. Palisaitis: None. R.E. Kearney: None. J.E. von Oettingen: None. L. Legault: Advisory Panel; Self; Dexcom, Inc., Lilly Diabetes. Research Support; Self; AstraZeneca K.K., Merck & Co., Inc., Sanofi-Aventis. Other Relationship; Self; Lilly Diabetes. A. Haidar: Consultant; Self; Eli Lilly and Company. Research Support; Self; Dexcom, Inc., Eli Lilly and Company. Funding Canadian Institutes of Health Research (356406)
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".