Association Between Change in Nonexercise Estimated Cardiorespiratory Fitness and Mortality in Men
Bibliographic record
Abstract
Objective To examine the association between change in nonexercise estimated cardiorespiratory fitness (eCRF) and mortality risk in adult men. Patients and Methods A total of 10,445 men (mean age, 44.6±9.3 years) from the Aerobics Center Longitudinal Study underwent 2 comprehensive medical examinations and peak work rate tests between January 1, 1979, and December 31, 2002, with an average time between measures of 5.7±4.9 years. Participants were observed for 11.6±6.4 years after their second examination until death or December 31, 2003. The eCRF was calculated with the Jackson et al (2012) and Nes et al (2011) published nonexercise estimation equations. Cox proportional hazards models were performed to examine the association between change in eCRF and all-cause and cardiovascular disease (CVD) mortality. Results There were 601 deaths (192 CVD deaths) during the follow-up period. For both eCRF equations, a higher eCRF at baseline was associated with significant reductions in mortality risk from all causes and CVD ( P <.001). Change in eCRF by the Jackson equation remained significantly associated with all-cause mortality ( P <.001) and CVD mortality ( P =.02) after multivariable adjustment. Every 1 metabolic equivalent (3.5 mL·kg −1 ·min −1 ) increase in eCRF was associated with a 21% and 22% reduction in mortality risk from all causes or CVD, respectively. No significant associations were observed between change in eCRF by the Nes equation and all-cause ( P =.69) or CVD ( P =.85) mortality risk after multivariable adjustment. Conclusion The association between change in nonexercise eCRF and mortality risk may be equation dependent.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".