Evoking Cutaneous Reflexes During Human Walking I. A Step‐by‐Step Methodological Approach
Bibliographic record
Abstract
This paper, the first in a two-part series focused on measuring cutaneous reflexes during human walking, provides a detailed step-by-step methodology for reliably eliciting cutaneous reflexes during human treadmill walking. The procedure addresses the technical challenges of eliciting reflexes from cutaneous nerves in a consistent and reproducible manner throughout the gait cycle. Building on approaches used in previous studies, we integrate practical guidance on equipment setup, electrode placement, configuration of a foot-sensitive resistor for quantifying gait cycle parameters, and reflex measurements to enable successful implementation across laboratories with varying levels of expertise. The custom development and use of a pseudorandomized stimulation approach is a novel feature of our broader methodology and is described in detail in the second paper. The present protocol focuses on the experimental setup required to obtain high-quality reflex measurements during walking, thereby providing the basis for advanced stimulation paradigms in human sensorimotor research. © 2025 Wiley Periodicals LLC. Basic Protocol: Evoking cutaneous reflexes during human walking using a pseudorandomized approach.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".