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Record W2005617438 · doi:10.1089/152091504322783440

Is Carotid Ultrasound a Useful Tool in Assessing Cardiovascular Disease in Individuals with Diabetes?

2004· review· en· W2005617438 on OpenAlexaff
Amish Parikh, Denis Daneman

Bibliographic record

VenueDiabetes Technology & Therapeutics · 2004
Typereview
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineDiabetes mellitusGlycemicCardiologyInternal medicineStroke (engine)Myocardial infarctionUltrasoundBlood pressureIntima-media thicknessDiseaseType 2 diabetesPopulationType 2 Diabetes MellitusCarotid arteriesRadiologyEndocrinology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.666
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.317
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations20
Published2004
Admission routes1
Has abstractyes

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