Numeracy skills and glycemic control in an observational, multi-centre, cross-sectional, and international study of children with Type 1 Diabetes
Bibliographic record
Abstract
AIMS: This study examined the possible association between numeracy skills and glycemic outcomes in children with type 1 diabetes. METHODS: The study used a cross-sectional design and collected data from 7 centers of the Hvidoere Study Group. HbA1c was measured centrally. Numeracy was assessed using the specific 5-item Diabetes Numeracy Test (DNT-5) and the international, general Wordless Mathematical Test (WMT). The HbA1c predictive multivariate generalized linear model was constructed using the adjusted R-squared index for model selection. Pearson's correlation coefficient was calculated between observed and predicted HbA1c levels in the training and testing datasets. RESULTS: Overall,306 adolescents aged 12-18 (mean age 14.96 ± 1.68) years and diabetes duration of 6.57 (±3.75) participated in this study. Numeracy skills, as assessed by the WMT but not DNT-5, predicted the HbA1c levels after adjustment for sociodemographic and clinical factors. The correlation between observed and predicted HbA1c levels was consistent in both datasets and was 0.34 (N = 155) and 0.37 (N = 61) for the training and test datasets, respectively (p = 0.412). The effect size for the WMT-based predictive model of HbA1c adjusted for clinical and socioeconomic factors was significantly higher (p < 0.05) than the single-parameter-based model. CONCLUSIONS: Numeracy, as assessed by an international general math test, is a good predictor of HbA1c in children and adolescents with type 1 diabetes. The basic and short WMT is a potentially effective tool in personalized clinical pediatric diabetes practice. Therapy planning should consider adjusting therapy to compensate for lower numeracy skills and/or training to improve the patient's numerical proficiency.
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How this classification was reachedexpand
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) | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".