Measuring tibial hemodynamics and metabolism at rest and after exercise using near-infrared spectroscopy
Bibliographic record
Abstract
The bone vascular system is important, yet evaluation of bone hemodynamics is difficult and expensive. This study evaluated the utility and reliability of near-infrared spectroscopy (NIRS), a portable and relatively inexpensive device, in measuring tibial hemodynamics and metabolic rate. Eleven participants were tested twice using post-occlusive reactive hyperemia technique with the NIRS probes placed on the tibia and the medial gastrocnemius (MG) muscle. Measurements were made at rest and after 2 levels of plantarflexion exercise. The difference between oxygenated and deoxygenated hemoglobin signal could be reliably measured with small coefficients of variation (CV; range 5.7–9.8%) and high intraclass correlation coefficients (ICC; range 0.73–0.91). Deoxygenated hemoglobin rate of change, a potential marker for bone metabolism, also showed good reliability (CV range 7.5–9.8%, ICC range 0.90–0.93). The tibia was characterized with a much slower metabolic rate compared with MG (p < 0.001). While exercise significantly increased MG metabolic rate in a dose-dependent manner (all p < 0.05), no changes were observed for the tibia after exercise compared with rest (all p > 0.05). NIRS is a suitable tool for monitoring hemodynamics and metabolism in the tibia. However, the local muscle exercise protocol utilized in the current study did not influence bone hemodynamics or metabolic rate. Novelty: NIRS can be used to monitor tibial hemodynamics and metabolism with good reliability. Short-duration local muscle exercise increased metabolic rate in muscle but not in bone. High level of loading and exercise volume may be needed to elicit measurable metabolic changes in bone.
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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 | 0.001 |
| 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".