Cardiorespiratory fitness estimations and their ability to predict all-cause mortality in patients with cardiovascular disease
Bibliographic record
Abstract
Background: In cardiac rehabilitation programs, cardiorespiratory fitness is commonly estimated (eCRF) from the maximum workload achieved on a graded exercise test. This study compared four well-established eCRF equations in their ability to predict mortality in patients with cardiovascular disease (CVD). Methods: A total of 7269 individuals with CVD were studied (81% male; age 59.4 ± 10.3yr). eCRF was calculated using equations from the American College of Sports Medicine, Bruce et al., the Fitness Registry and the Importance of Exercise International Database, and McConnell and Clark. The eCRF from each equation was compared with a RMANOVA. Cox proportional hazard models assessed the relationship between the eCRF equations and mortality risk. The predictive ability of the models was compared using the concordance index. Results: < 0.05). The concordance index values for each of the models were the same (0.77) indicating similar predictive performance. Conclusions: The four well-established eCRF equations did not differ in their ability to predict mortality in patients with CVD, indicating any could be used for this purpose. However, the differences in eCRF from each of the equations suggest potential differences in their ability to guide clinical care and should be the focus of future research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".