Rethinking Carbohydrate Intake and Time in Range in Children and Adolescents with Type 1 Diabetes
Bibliographic record
Abstract
The aim of this study was to evaluate the association between macronutrient intake and time in range (TIR) of 70–180 mg/dL in children and adolescents with type 1 diabetes (T1D). A multi-center study recruited patients with T1D using continuous glucose monitoring (CGM) between January 2019 and January 2020 from centers across Italy. Diet intake was recorded using three-day weighed food diaries. Nutrients were evaluated as percentages of total intake. TIR was considered at target if the percentage of readings was higher than 70%. Clinical and nutritional factors associated with TIR at target were analyzed using multiple correspondence analysis and multiple logistic regression. Data from 197 participants (53% male, median age 11.6 years, median HbA1c 55.2 mmol/mol, median TIR 60%) were analyzed. Macronutrient intake was 45.9% carbohydrates, 16.9% protein, 37.3% fat, and 13.1 g/day fiber (median values). TIR > 70% was observed in 28% of participants; their diet contained more protein (17.6%, p = 0.015) and fiber (14.4 g/day, p = 0.031) than those not at target. The probability of having a TIR > 70% was significantly higher with 40–44% consumption of carbohydrates compared with 45–50% consumption of carbohydrates and with the use of a carbohydrate counting system. Based on these results, a five percent reduction in the percentage of carbohydrate intake can help children and adolescents with T1D achieve the goal of a TIR > 70%. Both a lower and higher percentage of carbohydrate intake appears to reduce the probability of reaching the target TIR > 70%. These results require validation in other populations before being used in clinical practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".