Quantifying leg muscle deoxygenation during incremental cycling in hypoxemic patients with fibrotic interstitial lung disease
Bibliographic record
Abstract
Abstract Background Hypoxaemia and cardiocirculatory abnormalities may impair muscle oxygen (O2) delivery relative to O2 requirements thereby increasing the rate of O2 extraction during incremental exercise in fibrotic interstitial lung disease (f‐ILD). Using changes in deoxyhaemoglobin concentration ([HHb]) by near‐infrared spectroscopy (NIRS) as a proxy of O2 extraction, we investigated whether a simplified (double‐linear) approach, previously tested in heart failure, would provide useful estimates of muscle deoxygenation in f‐ILD. Methods A total of 25 patients (23 men, 72 ± 8 years; 20 with idiopathic pulmonary fibrosis, lung diffusing capacity for carbon monoxide = 44 ± 11% predicted) and 12 age‐ and sex‐matched healthy controls performed incremental cycling to symptom limitation. Changes in vastus lateralis [HHb] assessed by NIRS were analysed in relation to work rate (WR) and O2 uptake throughout the exercise. Results Patients showed lower exercise capacity than controls (e.g., peak WR = 67 ± 18% vs. 105 ± 20% predicted, respectively; p < 0.001). The [HHb] response profile was typically S‐shaped, presenting three distinct phases. Exacerbated muscle deoxygenation in patients versus controls was evidenced by: (i) a steeper mid‐exercise [HHb]‐WR slope (0.30 ± 0.22 vs. 0.11 ± 0.08 μmol/W; p = 0.008) (Phase 2), and (ii) a larger late‐exercise increase in [HHb] (p = 0.002) (Phase 3). Steeper [HHb]‐WR slope was associated with lower peak WR (r = –0.70) and greater leg discomfort (r = 0.77; p < 0.001) in f‐ILD. Conclusion This practical approach to interpreting [HHb] during incremental exercise might prove useful to determine the severity of muscle deoxygenation and the potential effects of interventions thereof in hypoxemic patients with f‐ILD.
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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 | 0.001 |
| 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) | 0.001 | 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".