The evolving role of cardiopulmonary exercise testing in ischemic heart disease – state of the art review
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".