Facilitating Cutaneous Afferent Feedback with Texture on Mechanically Induced Stretch Reflex Excitability During Gait Termination
Bibliographic record
Abstract
Cutaneous feedback plays a large role in the reflexive activation of muscle activity (Ia stretch reflex) generating postural responses during planned gait termination. As the mechanisms to induce a cutaneous afferent volley have been limited to electrical stimulation, it remains unknown if mechanical stimuli can modulate stretch reflex (SR) excitability. The purpose of this study was to examine the effect of adding cutaneous inputs on modulating the SR during perturbed gait termination. Thirty young adults completed walking trials when a platform unexpectedly tilted 10 degrees anteriorly or posteriorly, inducing a tibialis anterior (TA) or medial gastrocnemius (MG) short-latency reflex. The SR latency, the peak SR amplitude, and the total SR amplitude of the agonist burst, were compared between the stretched muscle and cutaneous facilitation. Statistically significant interactions were observed between the stretched muscle and cutaneous facilitation on the SR peak and SR burst. More notably, texture resulted in a consistent expression on the TA SR magnitude, without a similar effect in MG. Despite confirming the ability of cutaneous afferent facilitation on modulating spinal interneuronal circuitry, participant variability in response to texture highlights the importance of focusing on individual participant results when studying the effects of cutaneous facilitation on modulating spinal motorneuron excitability.
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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) | 0.002 | 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".