Pulmonary diffusion and aerobic capacity: is there a relation? Does obesity matter?
Bibliographic record
Abstract
AIM: We sought to determine whether pulmonary diffusing capacity for nitric oxide (DLNO), carbon monoxide (DLCO) and pulmonary capillary blood volume (Vc) at rest predict peak aerobic capacity (VO2peak), and if so, to discern which measure predicts better. METHODS: Thirty-five individuals with extreme obesity (body mass index or BMI = 50 +/- 8 kg m((-2)) and 26 fit, non-obese subjects (BMI = 23 +/- 2 kg m((-2)) participated. DLNO and DLCO at rest were first measured. Then, subjects performed a graded exercise test on a cycle ergometer to determine (VO2peak). Multivariate regression was used to assess relations in the data. RESULTS: Findings indicate that (i) pulmonary diffusion at rest predicts (VO2peak) in the fit and obese when measured with DLNO, but only in the fit when measured with DLCO; (ii) the observed relation between pulmonary diffusion at rest and (VO2peak) is different in the fit and obese; (iii) DLNO explains (VO2peak) better than DLCO or Vc. The findings imply the following reference equations for DLNO: (VO2peak) (mL kg(-1) min(-1)) = 6.81 + 0.27 x DLNO for fit individuals; (VO2peak) (mL kg(-1) min(-1)) = 6.81 + 0.06 x DLNO, for obese individuals (in both groups, adjusted R(2 )=( )0.92; RMSE = 5.58). CONCLUSION: Pulmonary diffusion at rest predicts (VO2peak), although a relation exists for obese subjects only when DLNO is used, and the magnitude of the relation depends on gender when either DLCO or Vc is used. We recommend DLNO as a measure of pulmonary diffusion, both for its ease of collection as well as its tighter relation with (VO2peak).
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".