Predictors of glucose control in children and adolescents with type 1 diabetes mellitus
Bibliographic record
Abstract
AIMS: To evaluate the glucose control [(as measured by hemoglobin A1c (HbA1c)], the factors associated with glycemic control, and possible explanations for these associations in a sample of children and adolescents with type 1 diabetes. METHODS: Data were collected on 155 children and adolescents, with type 1 diabetes mellitus, attending a multidisciplinary diabetes clinic in Portland, OR. Patients' hospital charts were reviewed to determine demographic factors, disease-related characteristics, and HbA1c level. RESULTS: Mean percent HbA1c was 9.3. Adolescents between the ages of 14 and 18 yr were in poorer metabolic control (adjusted mean percent HbA1c 0.56 higher than children 2-8 yr). Children who attended the clinic three to four times in the previous year were in better control (adjusted mean percent HbA1c 0.46 lower than those who visited two or fewer times and 1.11 lower than those who attended five or more times). Children with married parents were in better glycemic control than those of single, separated, or divorced parents (adjusted mean percent HbA1c 0.47 lower for children of married parents). This effect appeared to be mediated, in part, by the number of glucose checks performed per day. CONCLUSIONS: This study suggests that adolescents should be targeted for improved metabolic control. Diabetes team members need to be aware of changing family situations and provide extra support during stressful times. Regular clinic attendance is an important component of intensive diabetes management. Strategies must be developed to improve accessibility to the clinic and to identify patients who frequently miss appointments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".