Carbohydrate Counting/Bolus Calculator Mobile Application Improves Time in Range in Adults with Type 1 Diabetes Subjects
Bibliographic record
Abstract
Objective: To evaluate the effectiveness and safety of a mobile application for carbohydrate counting and bolus calculation (CHOC-BC) in adults with type 1 diabetes mellitus (T1DM). Research Design and Methods: A 12-week randomized controlled trial was conducted at King Fahad Medical City, Riyadh, Saudi Arabia. Adults with T1DM on multiple daily insulin injections and using Libre 2 glucose monitors were randomized to either CHOC-BC or conventional treatment. The primary endpoint was time in range (TIR; 70–180 mg/dL). Results: A total of 127 participants (70 females) were included: 64 in the intervention group and 63 in the control group with a mean age of 26.56 ± 4.8 and 26.74 ± 6.52 years, respectively. After 3 months, the intervention group achieved better TIR than the control group (51.20% ± 11.61% vs. 46.17% ± 13.02%; mean difference [MD], 5.03; 95% confidence interval [CI], 0.70–9.36; P = 0.023). Application users showed a significant reduction in level 2 time above range (17.25% ± 11.61% vs. 24.10% ± 15.74%; MD, −6.85; 95% CI, −11.70 to −1.99; P = 0.006). No significant differences were observed in body weight or time below range. Conclusions: The CHOC-BC mobile application empowered users to achieve better glycemic control while maintaining a safe profile that avoids hypoglycemia and weight gain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".