Correlates of glycemic control and quality of life outcomes in adolescents with type 1 diabetes
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Bibliographic record
Abstract
BACKGROUND: A major focus of pediatric multidisciplinary diabetes care is promoting glycemic control (A1c) while ensuring high quality of life (QOL). The current study investigated factors associated with A1c and QOL using a methodology that considered these variables as simultaneous outcomes. METHOD: A total of 261 adolescents (aged 13-18) with type 1 diabetes completed measures of blood glucose monitoring (BGM) frequency, diabetes-specific QOL, negative affect, and depression. Caregivers completed measures of demographic and disease characteristics, depression, and family conflict. RESULTS: A1c was negatively correlated with QOL (r = -0.18 to -0.29, p < 0.01) across all subscales. Based on clinical A1c goals and median QOL scores, adolescents fell into four glycemic control-QOL groups. Multinomial logistic regression determined correlates of group membership utilizing adolescents with suboptimal glycemic control-low QOL as the referent group. Adolescents with optimal glycemic control-high QOL reported increased BGM frequency (OR = 1.87), less negative affect (OR = 1.32), and were more likely to use CSII (OR = 5.41). Adolescents with optimal A1c-low QOL reported greater BGM frequency (OR = 1.91) and shorter disease duration (OR = 1.09). Adolescents with suboptimal glycemic control-high QOL reported greater BGM frequency (OR = 1.41), fewer depressive symptoms (OR = 1.13), and less negative affect (OR = 1.31). CONCLUSIONS: Results reveal disease, management, and psychosocial characteristics that differentiate glycemic control-QOL outcome groups and identify risk factors related to this relationship. Further appreciation of these characteristics may increase clinicians' understanding and attention to these important clinical outcomes and help tailor the most appropriate interventions (e.g., individual psychotherapy vs. family problem-solving interventions) to help adolescents achieve glycemic control without sacrificing QOL.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it