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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".