The effectiveness of glucose, sucrose, and fructose in treating hypoglycemia in children with type 1 diabetes
Bibliographic record
Abstract
OBJECTIVE: There is a lack of evidence regarding the most effective treatment option for managing naturally occurring hypoglycemia in children with type 1 diabetes. The objectives of this study were (i) to determine if sucrose and fructose are equally effective as glucose in the treatment of spontaneous hypoglycemia in children with type 1 diabetes; and (ii) to determine prestudy and poststudy hypoglycemia treatment preferences. METHODS: Thirty-three subjects [aged 5.4-15.5 yr and average duration of type 1 diabetes of 3.1 yr (SD = 2.3)] participated in a randomized, crossover design. The main outcome was the effectiveness of treatment as defined by a blood glucose meter reading that was > or = 4.0 mmol/L 15 min after treatment. Each subject treated five hypoglycemic events with each treatment type: glucose (BD Glucose Tablets), sucrose (Skittles), and fructose (Fruit to Go). RESULTS: There was a significant difference between the effectiveness of the three treatments [Wilk's Lambda F(2,28) = 8.64, p = 0.001]. No significant difference between treatment with glucose and treatment with sucrose was noted, but the treatment effectiveness for fructose was significantly lower than sucrose [F (1,29) = 16.09, p < 0.001] and glucose [F (1,29) = 15.64, p < 0.001]. The preferred treatment choices before the study were as follows: 36% glucose, 18% sucrose, and 33% fructose sources. Poststudy, 52% of children preferred the same treatment, which was effective (glucose or sucrose), followed by 35% who changed their preference to an effective treatment. CONCLUSION: Skittles are as effective in treating hypoglycemia as more expensive BD Glucose Tablets in children with type 1 diabetes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".