The use of CGM to identify hypoglycemia and glycemic patterns in congenital hyperinsulinism
Bibliographic record
Abstract
Abstract Objectives Unrecognized hypoglycemia, especially in the neonatal population, is a significant cause of morbidity and poor neurologic outcomes. Children with congenital hyperinsulinism (HI) are at risk of hypoglycemia and point of care testing (POCT) is the standard of care. Studies have shown that continuous glucose monitoring (CGM) improves glycemic control and reduces the frequency of hypoglycemia among children with type 1 diabetes. There is limited experience with the use of CGM in children with HI. To assess the glycemic pattern of children with HI on stable therapy and evaluate the frequency of undetected hypoglycemia using Dexcom G6® CGM. Methods A cross-sectional, observational pilot study was done in 10 children, ages 3 months to 17 years. Each child had a clinical or genetic diagnosis of HI on stable medical therapy. Participants were asked to continue their usual POCT blood glucose monitoring, as well as wear a blinded Dexcom G6® CGM during a 20-day study period with the potential of unblinding if there was severe hypoglycemia detected during the study trial. Results During the study period, 26 hypoglycemic events were noted by CGM in 60 % of the participants with 45 % occurring between 0600 and 0800. Conclusions CGM can help detect hypoglycemia and blood glucose trends during a time when there is usually no POCT, which can guide medical management. 30 % of our population had a dose adjustment in their medications. This study was limited by population size.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".