Consistency of Quantitative Scores of Hypoglycemia Severity and Glycemic Lability and Comparison with Continuous Glucose Monitoring System Measures in Long-Standing Type 1 Diabetes
Bibliographic record
Abstract
BACKGROUND: In long-standing type 1 diabetes (T1D), loss of endogenous insulin secretion and glucose dysregulation can lead to severe hypoglycemia and associated complications. Here, we report the serial consistency and the correlation between different scores that characterize glucose dysregulation using self-monitoring of blood glucose (SMBG), in a cohort of T1D individuals being evaluated for transplant eligibility in Clinical Islet Transplantation Consortium trials. SUBJECTS AND METHODS: In total, 152 C-peptide-negative T1D subjects with at least one severe hypoglycemia episode in the prior year documented SMBG at enrollment and every 6 months until deemed ineligible or transplanted. SMBG was used to calculate the HYPO score, Lability Index (LI), and mean amplitude of glycemic excursion (MAGE). Additionally, a blinded continuous glucose monitoring system (CGMS) was worn for 72 h at enrollment and every 12 months. RESULTS: In this cohort, LI was the most consistent (intraclass correlation coefficient=0.70) over time, followed by the HYPO score (0.51), with MAGE being the least consistent (0.36). Although MAGE and LI were highly correlated with each other, neither correlated with CGMS SD or glucose coefficient of variation (CV). Subjects spent a median of 97 min/day at <54 mg/dL using CGMS. The HYPO score correlated with CGMS time below 54 mg/dL and glucose CV. CONCLUSIONS: The HYPO score and LI are more consistent than MAGE in patients with established T1D experiencing severe hypoglycemic events and may be especially useful both for identifying subjects experiencing the greatest difficulty in maintaining glycemic control and for longitudinal assessment of novel interventions.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 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".