Deoxygenation of inspiratory muscles during cycling, hyperpnoea and loaded breathing in health and disease: a systematic review
Bibliographic record
Abstract
Summary Assessing inspiratory muscle deoxygenation and blood flow can provide insight into anaerobic stress, recruitment strategies and mechanisms of inspiratory muscle limitation. Therefore, this review aimed to synthesize measurements of inspiratory muscle oxyhaemoglobin (O2Hb), deoxyhaemoglobin (HHb), blood volume and flow of the inspiratory muscles acquired via near‐infrared spectroscopy (NIRS) during cycling, hyperpnoea and loaded breathing in healthy non‐athletes, healthy athletes and patients with chronic obstructive pulmonary disease (COPD) or chronic heart failure (CHF). Searches were performed on Medline and Medline in‐process, EMBASE, Central, Sportdiscus, PubMed and Compendex. Reviewers independently abstracted articles and assessed their quality using the modified Downs and Black checklist. Of the 644 articles identified, 21 met the inclusion criteria. Studies evaluated non‐athletes (n = 9), athletes (n = 5), COPD (n = 2) and CHF (n = 5). The sample was 90% male and 73% were non‐athletes and athletes. Interventions included cycle ergometry, hyperpnoea, loaded breathing, elbow flexor loading and combined loaded breathing and ergometry. Athletes and patients with CHF or COPD demonstrated deoxygenation of inspiratory accessory muscles that was often an opposite or exaggerated pattern compared to non‐athletes. O2Hb decreased and HHb increased significantly in inspiratory muscles during cycle ergometry and loaded breathing with accentuated changes during combined ergometry and loaded breathing. During different regimens of hyperpnoea or loaded breathing, comparisons of inspiratory muscles demonstrated that the sternocleidomastoid deoxygenated more than the intercostals, parasternals or scalenes. Evaluating inspiratory muscle deoxygenation via NIRS can inform mechanisms of inspiratory muscle limitation in non‐athletes, athletes and patients with CHF or COPD.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".