Analysis of oxygen uptake efficiency parameters in young people with cystic fibrosis
Bibliographic record
Abstract
This study characterised oxygen uptake efficiency (OUE) in children with mild-to-moderate cystic fibrosis (CF). Specifically, it investigated (1) the utility of OUE parameters as potential submaximal surrogates of peak oxygen uptake ( $$\dot {V}{{\text{O}}_{2{\text{peak}}}}$$ ), and (2) the relationship between OUE and disease severity. Cardiopulmonary exercise test (CPET) data were collated from 72 children [36 CF, 36 age- and sex-matched controls (CON)], with OUE assessed as its highest 90-s average (plateau; OUEP), the gas exchange threshold (OUEGET) and respiratory compensation point (OUERCP). Pearson’s correlation coefficients, independent t tests and factorial ANOVAs assessed differences between groups and the use of OUE measures as surrogates for $$\dot {V}{{\text{O}}_{2{\text{peak}}}}$$ . A significant (p < 0.05) reduction in allometrically scaled $$\dot {V}{{\text{O}}_{2{\text{peak}}}}$$ and all OUE parameters was found in CF. Significant (p < 0.05) correlations between measurements of OUE and allometrically scaled $$\dot {V}{{\text{O}}_{2{\text{peak}}}}$$ , were observed in CF (r = 0.49–0.52) and CON (r = 0.46–0.52). Furthermore, measures of OUE were significantly (p < 0.05) correlated with pulmonary function (FEV1%predicted) in CF (r = 0.38–0.46), but not CON (r = −0.20–0.14). OUEP was able to differentiate between different aerobic fitness tertiles in CON but not CF. OUE parameters were reduced in CF, but were not a suitable surrogate for $$\dot {V}{{\text{O}}_{2{\text{peak}}}}$$ . Clinical teams should, where possible, continue to utilise maximal CPET parameters to measure aerobic fitness in children and adolescents with CF. Future research should assess the prognostic utility of OUEP as it does appear sensitive to disease status and severity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".