Lipid profile is associated with treatment regimen in a large cohort of children and adolescents with Type 1 diabetes mellitus: a study from the international <scp>SWEET</scp> database
Bibliographic record
Abstract
Abstract Aims To examine the effect of pump vs injection therapy on the lipid profile of children with Type 1 diabetes mellitus. Methods A cross‐sectional analysis of the lipid profile of children aged ≤ 18 years with Type 1 diabetes mellitus from SWEET, an international diabetes registry, was conducted with a focus on the effect of treatment regimen. Dyslipidaemia was defined as LDL cholesterol ≥2.6 mmol/l or non‐HDL cholesterol ≥3.1 mmol/l. LDL and non‐HDL cholesterol values among 14 290 children (52% boys, 51% receiving pump therapy) from 60 SWEET centres were analysed by linear and logistic regression analysis adjusted for sex, age, diabetes duration, HbA1c and BMI‐standard deviation score group, region, and common interactions between age, sex, HbA1c and BMI. Results This study confirmed the established associations of increased lipids with female sex, age, diabetes duration, HbA1c and BMI. LDL and non‐HDL cholesterol levels were lower in the pump therapy group compared to the injection therapy group [LDL cholesterol: injection therapy 2.44 mmol/l (95% CI 2.42 to 2.46) vs pump therapy 2.39 mmol/l (95% CI 2.37–2.41), P<0.001; non‐HDL cholesterol: injection therapy 2.88 mmol/l (95% CI 2.86 to 2.90) vs pump therapy 2.80 mmol/l (95% CI 2.78–2.82), both P<0.0001]. Similarly, the odds ratios for LDL cholesterol ≥2.6 mmol/l [0.89 (95% CI 0.82–0.97)] and non‐HDL cholesterol ≥3.1 mmol/l [0.85 (0.78 to 0.93)] were significantly lower in the pump therapy group, even after all adjustments. Conclusions Our results indicate that pump therapy is associated with a better lipid profile.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".