Effects of different modalities of afferent stimuli of the lumbo‐sacral area on control of lumbar paravertebral muscles
Bibliographic record
Abstract
Somatosensory feedback to the central nervous system is essential to plan, perform and refine spine motor control. However, the influence of somatosensory afferent input from the trunk on the motor output to trunk muscles has received little attention. The objective was to compare the effects of distinct modalities of afferent stimulation on the net motoneuron and corticomotor excitability of paravertebral muscles. Fourteen individuals were recruited. Modulation of corticospinal excitability (motor-evoked potential [MEP]) of paravertebral muscles was measured when afferent stimuli (cutaneous noxious and non-noxious, muscle contraction) were delivered to the trunk at 10 intervals prior to transcranial magnetic stimulation. Each peripheral stimulation was applied alone, and subsequent electromyography (EMG) modulation was measured to control for net motoneuron excitability. MEP modulation and MEP/EMG ratio were used as measures of corticospinal excitability with and without control of net motoneuron excitability, respectively. MEP and EMG modulation were smaller after evoked muscle contraction than after cutaneous noxious and non-noxious stimuli. MEP/EMG ratio was not different between stimulation types. Both MEP and EMG amplitudes were reduced after evoked muscle contraction, but not when expressed as MEP/EMG ratio. Noxious and non-noxious stimulation had limited impact on all variables. Distinct modalities of peripheral afferent stimulation of the lumbo-sacral area differently modulated responses of paravertebral muscles, but without an influence on corticospinal excitability with control of net motoneuron excitability. Muscle stimulation reduced paravertebral activity and was best explained by spinal mechanisms. The impact of afferent stimulation on back muscles differs from the effects reported for limb muscles.
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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".