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Record W4389159397 · doi:10.5551/jat.64531

The Association between the Level of Ankle-Brachial Index and the Risk of Poor Physical Function in Patients with Cardiovascular Disease

2023· article· en· W4389159397 on OpenAlexaff
Shota Uchida, Kentaro Kamiya, Nobuaki Hamazaki, Kohei Nozaki, Takafumi Ichikawa, Masashi Yamashita, Takumi Noda, Kensuke Ueno, Kazuki Hotta, Emi Maekawa, Minako Yamaoka‐Tojo, Atsuhiko Matsunaga, Junya Ako

Bibliographic record

VenueJournal of Atherosclerosis and Thrombosis · 2023
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsUniversity of Ottawa
FundersResearch Institute of Science and Technology for SocietyJapan Society for the Promotion of Science
KeywordsMedicineInterquartile rangeLogistic regressionInternal medicineAnkleDiseasePhysical therapyCardiologySurgery

Abstract

fetched live from OpenAlex

AIMS: The progression of atherosclerosis and decline in physical function are poor prognostic factors in patients with cardiovascular disease (CVD). The ankle-brachial index (ABI) is a widely used indicator of the degree of progression of atherosclerosis, which may be used to identify patients with CVD who are at risk of poor physical function. This study examined the association between ABI and poor physical function in patients with CVD. METHODS: We reviewed the data of patients with CVD who completed the ABI assessment and physical function tests (6-min walking distance, gait speed, quadriceps isometric strength, and short physical performance battery). Patients were divided into five categories according to the level of ABI, and the association between ABI and poor physical function was examined using multiple logistic regression analysis. Additionally, restricted cubic splines were used to examine the nonlinear association between ABI and physical function. RESULTS: A total of 2982 patients (median [interquartile range] age: 71[62-78] years, 65.8% males) were included in this study. Using an ABI range of 1.11-1.20 as a reference, logistic regression analysis showed that ABI ≤ 1.10 was associated with poor physical function. The restricted cubic spline analysis showed that all physical functions increased with an increase in ABI level. The increase in physical function plateaued at an ABI level of approximately 1.1. CONCLUSIONS: ABI may be used to identify patients with poor physical function. ABI levels below 1.1 are potentially associated with poor physical function in patients with CVD.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.167

Codex and Gemma teacher scores by category

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

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 head0.027
GPT teacher head0.238
Teacher spread0.212 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

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