Many patients with Type 1 diabetes estimate their prandial insulin need inappropriately
Bibliographic record
Abstract
BACKGROUND: Many factors contribute to the need for prandial insulin in Type 1 diabetes. However, patients' success in achieving normal postprandial glucose concentration is understudied. The aim of the present study was to determine how often patients with Type 1 diabetes achieve normal postprandial glucose concentrations and to evaluate factors associated with postprandial hypo- and hyperglycemia. METHODS: Data on food intake, physical activity, insulin administration, and blood glucose concentration were collected using a self-administered questionnaire from 331 patients with Type 1 diabetes (43% men; mean age 49 ± 12 years; mean diabetes duration 32 ± 13 years). Of these, 179 provided data on blood glucose concentrations measured 110-150 min postprandially. One such meal per patient was randomized for analyses. RESULTS: Hypoglycemia (< 4.0 mmol/L), normoglycemia (4.0-7.9 mmol/L), and hyperglycemia (≥ 8.0 mmol/L) were observed after 23%, 36%, and 41% of meals, respectively. The three postprandial glycemia groups did not differ with respect to the meal composition or the timing of the postprandial blood glucose measurement. In women, postprandial hyperglycemia was associated with shorter diabetes duration and higher preprandial blood glucose concentration, whereas postprandial hypoglycemia was associated with higher physical activity. No single factor explained the postprandial glycemic state in men. CONCLUSIONS: A total of 64% of patients estimated their prandial insulin need inappropriately, suggesting that estimation of the optimal prandial insulin dose is not easy, even after a long duration of 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".