Wireless near-infrared spectroscopy of skeletal muscle oxygenation and hemodynamics during exercise and ischemia
Bibliographic record
Abstract
The majority ofin vivoapplications of near-infrared spectroscopic (NIRS) monitoring use continuous wave instruments that require a fiberoptic cable connection between the subject and the instrument during monitoring. In studies of muscle physiology where subjects are exercising, and particularly in those who are engaged in sports activity, a wireless instrument with telemetric capacity provides obvious advantages. Having access to reliable telemetric NIRS technology will also increase the practicality and scope of this biomedical monitoring technique in clinical settings. We report the feasibility of using a wireless continuous wave NIRS instrument with light emitting diodes, spatially resolved configuration, and Bluetooth®capability to study skeletal muscle oxygenation and hemodynamics during isometric contraction and ischemia induction. In ten healthy subjects comparable patterns of change in chromophore concentration (oxygenated and deoxygenated hemoglobin), total hemoglobin and muscle oxygen saturation were observed during 3 sets of isometric voluntary forearm muscle contraction at 10, 30 and 50% of maximum voluntary capacity (MVC), and a period of ischemia generated subsequently. This small series indicates that data with good intra- and inter-subject reproducibility that is free of movement artifact can be obtained with the wireless NIRS instrument described. The validity of these muscle studies demonstrate a basis for applying wireless NIRS monitoring to publisher-id biomedical applications.
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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 | 0.001 |
| 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".