A practical clinical approach to utilize cardiopulmonary exercise testing in the evaluation and management of coronary artery disease
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is growing clinical interest for the use of cardiopulmonary exercise testing (CPET) to evaluate patients with or suspected coronary artery disease (CAD). With mounting evidence, this concise review with relevant teaching cases helps to illustrate how to integrate CPET data into real world patient care. RECENT FINDINGS: CPET provides a novel and purely physiological basis to identify cardiac dysfunction in symptomatic patients with both obstructive-CAD and nonobstructive-CAD (NO-CAD). In many cases, abnormal cardiac response on CPET may be the only objective evidence of potentially undertreated ischemic heart disease. When symptomatic patients have NO-CAD on coronary angiogram, they are still at increased risk for cardiovascular events. This problem appears to be more common in women than men and may warrant more aggressive risk factor modification. As the main intervention is lifestyle (diet, smoking cessation, exercise) and medical therapy (statins, angiotensin-converting enzyme inhibitors, beta-blockers), serial CPET testing enables close surveillance of cardiovascular function and is responsive to clinical status. SUMMARY: CPET can enhance outpatient evaluation and management of CAD. Diagnostically, it can help to identify physiologically significant obstructive-CAD and NO-CAD in patients with normal routine cardiac testing. CPET may be of particular value in symptomatic women with NO-CAD. Prognostically, precise quantification of improvements in exercise capacity may help to improve long-term lifestyle and medication adherence for this chronic condition.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".