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Record W4402147407 · doi:10.14740/cr1669

Remote Ischemic Conditioning Improves Cardiovascular Function in Heart Failure Patients

2024· article· en· W4402147407 on OpenAlexvenueno aff
Miin-Yaw Shyu, Andrew Ying‐Siu Lee

Bibliographic record

VenueCardiology Research · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Ischemia and Reperfusion
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCardiologyHeart failureInternal medicineDisease

Abstract

fetched live from OpenAlex

Background: Recently, it has been shown that remote ischemic conditioning (RIC) can be used as a healthy regimen to reverse disease and aging. With this in mind, we are studying the consequences of RIC on cardiovascular function in heart failure patients. Methods: Forty patients with stable heart failure were prospectively enlisted and randomly divided into RIC (n = 20) and control (n = 20) groups. The RIC protocol consists of a 3-min inflation and then deflation of the blood pressure cuff attached to the upper arm to produce transient ischemia of the arm. RIC treatment was performed once daily for 1 year. NYHA class, left ventricular ejection fraction (LVEF), left atrial and ventricular dimensions were all assessed in two groups. Results: RIC was well tolerated. After 1 year of treatment, New York Heart Association (NYHA) class improved and LVEF showed a significant increase from 37.11% to 52.44% (P < 0.0001). Additionally, the dimensions of the left atrium (from 50.55 to 43.25 mm) and ventricle (from 53.04 to 50.15 mm) were significantly reduced in the RIC group. Conclusion: This study suggests that 1 year of RIC treatment as a health strategy could improve cardiovascular function in heart failure patients, leading to its widespread use in these patients.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.567
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.321
Teacher spread0.299 · 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 designNot applicable
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

Citations2
Published2024
Admission routes1
Has abstractyes

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