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Record W2142164422 · doi:10.1001/jama.295.2.180

International Prevalence, Recognition, and Treatment of Cardiovascular Risk Factors in Outpatients With Atherothrombosis

2006· article· en· W2142164422 on OpenAlexfundno aff
Deepak L. Bhatt

Bibliographic record

VenueJAMA · 2006
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsnot available
FundersFuwai Hospital, Chinese Academy of Medical SciencesDebreceni EgyetemUniversitas IndonesiaUniversity of IoanninaNational Taiwan University HospitalCleveland ClinicBrigham and Women's HospitalUniversity of TorontoErasmus Medisch CentrumKeio UniversityHuashan HospitalNational Taiwan UniversityUniversity of MinnesotaMinneapolis Heart Institute FoundationUniversity of South CarolinaMonash UniversityMinneapolis Heart InstituteNorthwestern University
KeywordsMedicineOverweightRisk factorInternal medicineCoronary artery diseaseDiabetes mellitusObesityDiseaseEndocrinology

Abstract

fetched live from OpenAlex

CONTEXT: Atherothrombosis is the leading cause of cardiovascular morbidity and mortality around the globe. To date, no single international database has characterized the atherosclerosis risk factor profile or treatment intensity of individuals with atherothrombosis. OBJECTIVE: To determine whether atherosclerosis risk factor prevalence and treatment would demonstrate comparable patterns in many countries around the world. DESIGN, SETTING, AND PARTICIPANTS: The Reduction of Atherothrombosis for Continued Health (REACH) Registry collected data on atherosclerosis risk factors and treatment. A total of 67,888 patients aged 45 years or older from 5473 physician practices in 44 countries had either established arterial disease (coronary artery disease [CAD], n = 40,258; cerebrovascular disease, n = 18,843; peripheral arterial disease, n = 8273) or 3 or more risk factors for atherothrombosis (n = 12,389) between 2003 and 2004. MAIN OUTCOME MEASURES: Baseline prevalence of atherosclerosis risk factors, medication use, and degree of risk factor control. RESULTS: Atherothrombotic patients throughout the world had similar risk factor profiles: a high proportion with hypertension (81.8%), hypercholesterolemia (72.4%), and diabetes (44.3%). The prevalence of overweight (39.8%), obesity (26.6%), and morbid obesity (3.6%) were similar in most geographic locales, but was highest in North America (overweight: 37.1%, obese: 36.5%, and morbidly obese: 5.8%; P<.001 vs other regions). Patients were generally undertreated with statins (69.4% overall; range: 56.4% for cerebrovascular disease to 76.2% for CAD), antiplatelet agents (78.6% overall; range: 53.9% for > or =3 risk factors to 85.6% for CAD), and other evidence-based risk reduction therapies. Current tobacco use in patients with established vascular disease was substantial (14.4%). Undertreated hypertension (50.0% with elevated blood pressure at baseline), undiagnosed hyperglycemia (4.9%), and impaired fasting glucose (36.5% in those not known to be diabetic) were common. Among those with symptomatic atherothrombosis, 15.9% had symptomatic polyvascular disease. CONCLUSION: This large, international, contemporary database shows that classic cardiovascular risk factors are consistent and common but are largely undertreated and undercontrolled in many regions of the world.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.237
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations1,616
Published2006
Admission routes1
Has abstractyes

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