Using broadband spatially resolved NIRS to assess muscle oxygenation during altered running protocols
Bibliographic record
Abstract
We used spatially resolved near-infrared spectroscopy (SRS-NIRS) to assess calf and thigh muscle oxygenation during running on a motor-driven treadmill. Two protocols were used: An incremental speed protocol (velocity = 6 - 12 km/h, ▵v = 2 km/h) was performed in 3 minute stages, while a pacing paradigm modulated step frequency alternatively (2.3 Hz [SLow]; 3.3 Hz [SHigh]) during a constant velocity for 2 minutes each. A SRS-NIRS broadband system (600 - 1000 nm) was used to measure total haemoglobin concentration and oxygen saturation (SO2). An accelerometer was placed on the hip joints to measure limb acceleration through the experiment. The data showed that the calf (SO2 58 to 42%) desaturated to a significantly lower level than the thigh (61 to 54%). During the pacing protocol, SO2 was significantly different between the SLow vs. SHigh trials. Additionally, physiological data as measured by spirometry were different between the SLow vs. SHigh pacing trials (VO2 (2563± 586 vs. 2503 ± 605 mL/min). Significant differences in VO2 at the same workload (speed) indicate alterations in mechanical efficiency. These data suggest that SRS broadband NIRS can be used to discern small changes in muscle oxygenation, making this device useful for metabolic exercise studies in addition to spirometry and movement monitoring by accelerometers.
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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 | 0.000 |
| 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".