Non-severe hypoglycemia in type 1 diabetes: a randomized crossover trial comparing two quantities of oral carbohydrates at different insulin-induced hypoglycemia ranges
Bibliographic record
Abstract
Aims Non-severe hypoglycemia (NS-H) is challenging for people living with type 1 diabetes (PWT1D) and often results from relative iatrogenic hyper-insulinemia. Current guidelines recommend a one-size-fits-all approach of 15–20 g of simple carbohydrates (CHO) every 15 min regardless of the triggering conditions of the NS-H event. We aimed to test different amounts of CHO to treat insulin-induced NS-H at various glucose ranges. Methods This is a randomized, four-way, crossover study involving PWT1D, testing NS-H treatment outcomes with 16 g vs. 32 g CHO at two plasma glucose (PG) ranges: A: 3.0–3.5 mmol/L and B: <3.0 mmol/L. Across all study arms, participants consumed an additional 16 g of CHO if PG was still <3.0 mmol/L at 15 min and <4.0 mmol/L at 45 min post-initial treatment. Subcutaneous insulin was used in a fasting state to induce NS-H. Participants had frequent venous sampling of PG, insulin, and glucagon levels. Results Participants (n = 32; 56% female participants) had a mean (SD) age of 46.1 (17.1) years, had HbA1c at 54.0 (6.8 mmol/mol) [7.1% (0.9%)], and had a diabetes duration of 27.5 (17.0) years; 56% were insulin pump users. We compared NS-H correction parameters between 16 g and 32 g of CHO for range A, 3.0–3.5 mmol/L (n = 32), and range B, <3.0 mmol/L (n = 29). Change in PG at 15 min for A: 0.1 (0.8) mmol/L vs. 0.6 (0.9) mmol/L, p = 0.02; and for B: 0.8 (0.9) mmol/L vs. 0.8 (1.0) mmol/L, p = 1.0. Percentage of participants with corrected episodes at 15 min: (A) 19% vs. 47%, p = 0.09; (B) 21% vs. 24%, p = 1.0. A second treatment was necessary in (A) 50% vs. 15% of participants, p = 0.001; (B) 45% vs. 34% of participants, p = 0.37. No statistically significant differences in insulin and glucagon parameters were observed. Conclusions NS-H, in the context of hyper-insulinemia, is difficult to treat in PWT1D. Initial consumption of 32 g of CHO revealed some advantages at the 3.0–3.5 mmol/L range. This was not reproduced at lower PG ranges since participants needed additional CHO regardless of the amount of initial consumption. Clinical trial registration ClinicalTrials.gov , identifier NCT03489967.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".