Methodological considerations on near-infrared spectroscopy derived muscle oxidative capacity
Bibliographic record
Abstract
Abstract Purpose Different strategies for near-infrared spectroscopy (NIRS)-derived muscle oxidative capacity assessment have been reported. This study compared and evaluated (I) approaches for averaging trials; (II) NIRS signals and blood volume correction equations; (III) the assessment of vastus lateralis (VL) and tibialis anterior (TA) muscles in two fitness levels groups. Methods Thirty-six participants [18 chronically trained (CT: 14 males, 4 females) and 18 untrained (UT: 10 males, 8 females)] participated in this study. Two trials of twenty transient arterial occlusions were performed for NIRS-derived muscle oxidative capacity assessment. Muscle oxygen consumption ( $$\dot{V}$$ V ˙ O2m) was estimated from deoxygenated hemoglobin (HHb), corrected for blood volume changes following Ryan (HHbR) and Beever (HHbB) equations, and from oxygen saturation (StO2) in VL and TA. Results Superimposing or averaging $$\dot{V}$$ V ˙ O2m or averaging the rate constants (k) from the two trials resulted in equivalent k values [two one-sided tests (TOST) procedure with 5% equivalence margin—P < 0.001]. Whereas HHbR (2.35 ± 0.61 min−1) and HHbB (2.34 ± 0.58 min−1) derived k were equivalent (P < 0.001), StO2 derived k (2.81 ± 0.92 min−1) was greater (P < 0.001) than both. k values were greater in CT vs UT in both muscles (VL: + 0.68 min−1, P = 0.002; TA: + 0.43 min−1, P = 0.01). Conclusion Different approaches for averaging trials lead to similar k. HHb and StO2 signals provided different k, although different blood volume corrections did not impact k. Group differences in k were detected in both muscles.
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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 | 0.090 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".