Steepest near‐infrared spectroscopy‐derived deoxygenation slopes during arterial occlusions provide more reliable assessments of muscle mitochondrial capacity
Bibliographic record
Abstract
Abstract Assessment of near‐infrared spectroscopy (NIRS)‐derived muscle oxidative capacity relies on analysing deoxygenation slopes from NIRS signal versus time curves during brief arterial occlusions, which reflect the rate of post‐exercise recovery of muscle oxygen consumption (). However, current guidelines lack recommendations on the optimal selection of slopes for reliable measurement. The aim of the study was to compare a standardised partial‐segment approach against the conventional whole‐segment approach on the measurement and reliability of in vivo muscle oxidative capacity. Within the same session, 19 athletes ( n = 9 sprinters; n = 10 middle‐distance runners) completed two NIRS‐derived muscle oxidative capacity trials on the vastus lateralis. Rate constants ( k , min −1 ) were computed using the steepest (), whole () or shallowest () deoxygenation slope from deoxyhaemoglobin (HHb) and muscle O 2 saturation () signals. Test–retest reliability [(coefficient of variation (CV), intraclass correlation coefficient (ICC)] and minimum difference (MD) were assessed. For the HHb signal, ICC analysis revealed moderate to excellent test–retest reliability for [0.80 (0.54–0.92)], whereas poor to good reliability was observed for [0.71 (0.38–0.89)] and [0.60 (0.19–0.83)]. led to lower MD compared to and (0.64 vs. 1.12 vs. 1.54 min −1 , respectively). All three approaches led to significantly greater k values in runners compared to sprinters (:+32.2%, P < 0.001; :+ 40.1%, P = 0.025; :+49.6%, P = 0.001). Compared to the conventional whole‐segment approach, selecting the steepest intra‐occlusion slope improved the reliability and sensitivity of NIRS‐derived mitochondrial capacity, likely by better reflecting instantaneous changes in .
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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 | 0.001 | 0.002 |
| 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".