Assessment of Clinical Factors Influencing Glucose Management Indicator and Glycated Hemoglobin Discordance in Children With Type 1 Diabetes: A 1-Year, Real-world Data Observation
Bibliographic record
Abstract
OBJECTIVES: Published data still highlight discordances between glucose management indicator (GMI; the parameter estimating glycated hemoglobin [A1C] from continuous glucose monitoring [CGM] reporting) and laboratory A1C, for reasons yet to be explored. In our study we aimed to identify potential clinical factors contributing to these discordances. METHODS: A retrospective study of 99 children (mean 12.92±4.03 years) was conducted using CGM devices (Dexcom G6-31, FreeStyle Libre 2-30, and Guardian 3-38). Inclusion criteria for patients were type 1 diabetes (T1D), continuous use of one type of CGM (with >70% sensor activity) over the previous year, and quarterly visits. At each visit, we collected data for age, sex, body mass index, diabetes duration, daily insulin dose, CGM report (14 of 90 days), and laboratory A1C. RESULTS: We confirmed linear dependency between A1C and GMI-that is, higher A1C led to more A1C-GMI differences. The A1C-GMI 90-day discordance was categorized into 4 thresholds: 48.7% at <0.25, 20.1% between 0.25 and 0.5, 22.4% between 0.5 and 0.75, and 8.7% at >0.75. Children with A1C-GMI 90 discordance <0.5% had significantly lower A1C (6.80% vs 7.59%), shorter T1D duration (<5 years), and more stable A1C (differences <0.4 between results). The analysis of participants' stability based on comparing A1C-GMI 90 discordances at subsequent follow-up visits confirmed an individual variability of <0.25 in two-thirds of participants. Other factors were not associated with the A1C-GMI discordance. CONCLUSIONS: One-year, real-world data show that clinically significant discordances (A1C-GMI 90 >0.5%) occurred in <30% of the children. A greater difference is more likely in individuals with higher A1C, longer diabetes duration, and less stable glycemic management. Individual A1C-GMI 90 discordance was mostly stable, although with varying degrees of difference.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".