Muscle and cerebral oxygenation during cycling in chronic obstructive pulmonary disease: A scoping review
Bibliographic record
Abstract
To synthesize evidence for prefrontal cortex (PFC), quadriceps, and respiratory muscle oxygenation using near-infrared spectroscopy (NIRS) during cycling in individuals with chronic obstructive pulmonary disease (COPD). A scoping review was performed searching databases (inception-August 2020): Ovid MEDLINE, EMBASE, Cochrane Systematic Reviews, Cochrane Central Register of Controlled Clinical Trials, CINAHL, SPORTDiscus and Pedro. The search focused on COPD, cycling, and NIRS outcomes. 29 studies (541 COPD participants) were included. Compared to healthy individuals (8 studies), COPD patients at lower cycling workloads had more rapid increases in vastus lateralis (VL) deoxygenated hemoglobin (HHb); lower increases in VL total hemoglobin (tHb) and blood flow; and lower muscle tissue saturation (StO 2 ). Heliox and bronchodilators were associated with smaller and slower increases in VL HHb. Heliox increased VL and intercostal blood flow compared to room air and supplemental oxygen in COPD patients (1 study). PFC oxygenated hemoglobin (O 2 Hb) increased in COPD individuals during cycling in 5 of 8 studies. Individuals with COPD and heart failure demonstrated worse VL and PFC NIRS outcomes compared to patients with only COPD—higher or more rapid increase in VL HHb and no change or decrease in PFC O 2 Hb. Individuals with COPD present with a mismatch between muscle oxygen delivery and utilization, characterized by more rapid increase in VL HHb, lower muscle O 2 Hb and lower muscle StO 2 . PFC O 2 Hb increases or tends to increase in individuals with COPD during exercise, but this relationship warrants further investigation. NIRS can be used to identify key deoxygenation thresholds during exercise to inform PFC and muscle oxygenation.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".