Bibliographic record
Abstract
Transcutaneous near infrared spectroscopy (NIRS) of muscle requires coupling between the device and the skin. An unfortunate by-product of this coupling is contact force artefact, where the amount of contact force between the device and the skin affects measurements. Contact force artefact is well known, but largely ignored in most NIRS research. We performed preliminary investigations of contact force artefact to quantify tissue behaviour to inform future NIRS designs. Specifically, we conducted three studies on contact force artefact: (i) an experimental investigation of static load at varied levels of contact force and muscle activation, (ii) an experimental investigation of oscillating load at varied levels of contact force and frequency, and (iii) a Monte Carlo simulation of photon propagation through skin, adipose tissue, and muscle. Our results confirmed that contact force artefact is a confounding factor in NIRS muscle measurements because contact force affects measured hemoglobin concentrations in a manner consistent with muscle contractions. Further, the effects of contact force are not altered by muscle contraction and a likely candidate for the mechanism responsible for contact force artefact is the viscoelastic compression of superficial tissues (skin and adipose) during loading. Simulation data suggests that adipose tissue plays a key role in diffuse reflectance of photons, so any compression of the superficial tissues will affect the reflected signal. Further research is required to fully understand the mechanisms behind contact force artefact, which will, in turn, inform future NIRS device designs.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".