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Record W2782121436 · doi:10.1161/circ.133.suppl_1.02

Abstract 02: American Heart Association (AHA)’s Life’s Simple 7 and Risk of Lower Extremity Peripheral Artery Disease (PAD): The Multi-Ethnic Study of Atherosclerosis (MESA)

2016· article· en· W2782121436 on OpenAlexaff
Christina L. Wassel, Laura J. Rasmussen‐Torvik, Alexis C. Wood, Matthew Allison, Mary Mcdermott, Aaron R. Folsom, Donald M. Lloyd‐Jones, Norrina B. Allen, Gregory L. Burke, Moysés Szklo, Michael H. Criqui, Mary Cushman

Bibliographic record

VenueCirculation · 2016
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsMount Allison University
Fundersnot available
KeywordsMedicineBody mass indexDemographyPopulationCohortProspective cohort studyProportional hazards modelAtherosclerosis Risk in CommunitiesInternal medicinePhysical therapyGerontologyEnvironmental health

Abstract

fetched live from OpenAlex

Background: Approximately 8.5 million Americans are affected by lower extremity PAD, which is associated with a substantially greater risk of cardiovascular (CV) events, mortality and functional decline. In 2010, the AHA initiated new CV health metrics (Life’s Simple 7) to monitor the goal of significantly improving CV health by the year 2020. The extent to which Life’s Simple 7 may be associated with risk of PAD or change in the ankle brachial index (ABI), the major clinical diagnostic criterion for PAD, has not been established. Methods: MESA is a population-based prospective cohort of 6814 Caucasian, African-American, Hispanic and Chinese men and women from six US field centers. The baseline exam occurred in 2000-02. Life’s Simple 7 at baseline included AHA definitions of poor, intermediate and ideal health behaviors (diet, body mass index, smoking, physical activity) and health factors (blood pressure, glucose, cholesterol), and was modeled continuously on a 0-14 point scale, with a higher score indicating better CV health. The scale was also categorized into overall inadequate (0-7points), average (8-11) and optimum (12-14) CV health. Incident PAD was defined as an ABI≤0.90 at Exam 3 (2004-05) or Exam 5 (2010-12), removing participants both with ABI>1.40 and prevalent PAD at baseline. Cox models were used for incident PAD analyses and change in the ABI over time was assessed using mixed models. All models were adjusted for age, sex and race/ethnicity. Results: The mean±SD Life’s Simple 7 score was 8.4±2.1, with 33.8% of participants classified as inadequate, 59.6% average, and 6.6% classified as having optimum CV health. Adjusted rates of PAD per 1000 person-years were 7.3 for inadequate, 3.2 for average and 1.1 for optimum CV health. Each point higher on the Life’s Simple 7 scale was associated with a 20% lower risk of incident PAD (95% CI (0.76-0.85); p<0.001). Compared to inadequate CV health, participants with average and optimum health had a 55% lower risk (95% CI (0.37-0.56), p<0.001) and an 85% lower risk (95% CI (0.06-0.38); p<0.001) of incident PAD, respectively. Each point higher on the scale was associated with a higher average ABI over a median follow-up time of 9.4 years (0.006 (95% CI (0.005, 0.007), p<0.001). Compared to those with inadequate health, participants with average and optimum health maintained a 0.021 (95% CI (0.016, 0.027, p<0.001) and a 0.030 (95% CI (0.018, 0.041, p<0.001) greater average ABI over the follow-up period, respectively. Conclusions: Maintaining average or optimum CV health results in a substantially reduced risk of incident PAD and also in maintaining a higher average ABI over time, even when accounting for age, sex and race/ethnicity. Encouraging improvement in health behaviors, and treatment to achieve and maintain better levels of CV health metrics, may contribute to decreasing the rate of PAD.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.025
GPT teacher head0.270
Teacher spread0.245 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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