Is Carotid Ultrasound a Useful Tool in Assessing Cardiovascular Disease in Individuals with Diabetes?
Bibliographic record
Abstract
Coronary heart disease is a major cause of morbidity and mortality in North America. Its prevention is therefore an important clinical goal. Individuals with both Type 1 and Type 2 diabetes mellitus are at increased risk of developing heart disease as compared with those without diabetes. Carotid ultrasound is now a well-validated tool to study the presence and progression of cardiovascular disease. Using ultrasound one can determine elastic properties of the vessel wall (distensibility and compliance) as well as intima-media thickness (IMT). Several large studies have shown that IMT is a useful predictor of future cardiovascular events such as myocardial infarction and stroke, and is well correlated with other traditional risk factors such as blood pressure, lipids, level of glycemic control, and smoking. For this reason, carotid ultrasound may add valuable clinical information above and beyond that provided by traditional risk factors. The use of carotid ultrasound in the pediatric and adolescent population is increasing, and one study has shown decreased distensibility in adolescents with Type 1 diabetes mellitus versus controls. However, IMT measurements in the children and teens with Type 1 diabetes have yielded conflicting results, and larger, longitudinal studies are needed in this area.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".