Peripheral artery tonometry demonstrates altered endothelial function in children with type 1 diabetes
Bibliographic record
Abstract
OBJECTIVES: To assess the ability of reactive hyperemia-peripheral artery tonometry (RH-PAT) to serve as a surrogate marker of endothelial dysfunction in children with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: Forty-four children with T1D [age 14.6 +/- 2.7 yr; duration of diabetes 6.01 +/- 4 yr; range of diabetes duration 1-16 yr; and hemoglobin A1c (HbA1c) 8.34 +/- 1.2%] and 20 children without diabetes (age 14.1 +/- 1.5 yr) underwent RH-PAT endothelial function testing after an overnight fast. Height, weight, body mass index (BMI), blood pressure (BP), fasting lipid profile, and glucose level were determined in each child. Children with T1D underwent a second RH-PAT study 4 wk after their initial study to determine the intrapatient variability of the technique. RESULTS: Children with T1D had endothelial dysfunction as evidenced by lower mean RH-PAT scores (1.63 +/- 0.5) when compared with children without diabetes (mean RH-PAT score 1.95 +/- 0.3) (p = 0.01). Repeat RH-PAT scores were predicted by initial RH-PAT scores (p = 0.0025). Mean intrapatient standard deviation of RH-PAT score was 0.261 and mean coefficient of variation was 14.8. Variations in RH-PAT score were not explained by differences in glucose, HbA1c, BMI, systolic BP, diastolic BP, or lipids. CONCLUSIONS: Although larger validation studies are required, RH-PAT is a promising non-invasive technique to assess endothelial function in children with T1D. Non-invasive measures of endothelial dysfunction may provide the additional risk stratification data needed to justify more aggressive primary prevention of cardiovascular disease in children with T1D.
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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) | 0.001 | 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 | 0.001 | 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".