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Record W4386084380 · doi:10.1097/hco.0000000000001086

The evolving role of cardiopulmonary exercise testing in ischemic heart disease – state of the art review

2023· review· en· W4386084380 on OpenAlexafffund
Sundeep Chaudhry, Naresh Kumar, Ross Arena, Subodh Verma

Bibliographic record

VenueCurrent Opinion in Cardiology · 2023
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
FundersAmarin CorporationNovo NordiskSanofiHLS TherapeuticsAmgenPfizerAstraZenecaEli Lilly and Company
KeywordsMedicineCardiorespiratory fitnessAsymptomaticDiseaseVO2 maxCardiologyIntensive care medicinePhysical therapyGold standard (test)Psychological interventionInternal medicineHeart rateBlood pressure

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Cardiopulmonary exercise testing (CPET) is the gold standard for directly assessing cardiorespiratory fitness (CRF) and has a relatively new and evolving role in evaluating atherosclerotic heart disease, particularly in detecting cardiac dysfunction caused by ischemic heart disease. The purpose of this review is to assess the current literature on the link between cardiovascular (CV) risk factors, cardiac dysfunction and CRF assessed by CPET. RECENT FINDINGS: We summarize the basics of exercise physiology and the key determinants of CRF. Prognostically, several studies have been published relating directly measured CRF by CPET and outcomes allowing for more precise risk assessment. Diagnostically, this review describes in detail what is considered healthy and abnormal cardiac function assessed by CPET. New studies demonstrate that cardiac dysfunction on CPET is a common finding in asymptomatic individuals and is associated with CV risk factors and lower CRF. This review covers how key CPET parameters change as individuals transition from the asymptomatic to the symptomatic stage with progressively decreasing CRF. Finally, a supplement with case studies with long-term longitudinal data demonstrating how CPET can be used in daily clinical decision making is presented. SUMMARY: In summary, CPET is a powerful tool to provide individualized CV risk assessment, monitor the effectiveness of therapeutic interventions, and provide meaningful feedback to help patients guide their path to improve CRF when routinely used in the outpatient setting.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.822
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.002
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 head0.083
GPT teacher head0.375
Teacher spread0.291 · 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 designOther design
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

Citations9
Published2023
Admission routes2
Has abstractyes

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