Reduced Short- and Long-Latency Afferent Inhibition Following Acute Muscle Pain: A Potential Role in the Recovery of Motor Output
Bibliographic record
Abstract
OBJECTIVE: Corticomotor output is reduced in response to acute muscle pain, yet the mechanisms that underpin this effect remain unclear. Here the authors investigate the effect of acute muscle pain on short-latency afferent inhibition, long-latency afferent inhibition, and long-interval intra-cortical inhibition to determine whether these mechanisms could plausibly contribute to reduced motor output in pain. DESIGN: Observational same subject pre-post test design. SETTING: Neurophysiology research laboratory. SUBJECTS: Healthy, right-handed human volunteers (n = 22, 9 male; mean age ± standard deviation, 22.6 ± 7.8 years). METHODS: Transcranial magnetic stimulation was used to assess corticomotor output, short-latency afferent inhibition, long-latency afferent inhibition, and long-interval intra-cortical inhibition before, during, immediately after, and 15 minutes after hypertonic saline infusion into right first dorsal interosseous muscle. Pain intensity and quality were recorded using an 11-point numerical rating scale and the McGill Pain Questionnaire. RESULTS: Compared with baseline, corticomotor output was reduced at all time points (p = 0.001). Short-latency afferent inhibition was reduced immediately after (p = 0.039), and long-latency afferent inhibition 15 minutes after (p = 0.035), the resolution of pain. Long-interval intra-cortical inhibition was unchanged at any time point (p = 0.36). CONCLUSIONS: These findings suggest short- and long-latency afferent inhibition, mechanisms thought to reflect the integration of sensory information with motor output at the cortex, are reduced following acute muscle pain. Although the functional relevance is unclear, the authors hypothesize a reduction in these mechanisms may contribute to the restoration of normal motor output after an episode of acute muscle pain.
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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) | 0.003 | 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".