Multivariate Criteria Most Accurately Distinguish Cardiac from Noncardiac Causes of Dyspnea
Bibliographic record
Abstract
Cardiopulmonary exercise testing provides oxygen pulse as a continuous measure of stroke volume, which is superior to other stress-testing methods in which systolic function is measured at baseline and at peak stress. However, the optimal peak oxygen pulse criterion for distinguishing cardiac from noncardiac causes of exercise limitation is unknown. In comparing several peak oxygen pulse criteria against the clinical standard of cardiopulmonary exercise testing, we retrospectively studied 54 consecutive patients referred for cardiopulmonary exercise testing. These exercise tests included measurement of oxygen consumption, carbon dioxide production, breathing reserve, arterial blood gases at baseline and at peak stress, exercise electrocardiogram, heart rate, and blood pressure response. Results were blindly interpreted and patients were categorized as members either of our Cardiac Group (abnormal result secondary to cardiac causes of exercise limitation) or of our Noncardiac Group (normal or abnormal result secondary to any noncardiac cause of exercise limitation). The accuracy of the peak oxygen pulse criteria ranged from 50% for univariate criterion (≤15 mL/beat), to 61% for oxygen pulse curve pattern, to 63% for bivariate criterion (≤15 mL/beat for men, ≤10 mL/beat for women), to as high as 81% for a multivariate criterion. All multivariate criteria outperformed oxygen pulse curve pattern, univariate, and bivariate criteria. This is the first study to evaluate the optimal peak oxygen pulse criterion for differentiating cardiac from noncardiac causes of exercise limitation. Multivariate criteria (especially a criterion incorporating age, sex, height, and weight) should be used preferentially, as opposed to the commonly used univariate and bivariate criteria.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".