Fatigue-related firing of distal muscle nociceptors reduces voluntary activation of proximal muscles of the same limb
Bibliographic record
Abstract
With fatiguing exercise, firing of group III/IV muscle afferents reduces voluntary activation and force of the exercised muscles. These afferents can also act across agonist/antagonist pairs, reducing voluntary activation and force in nonfatigued muscles. We hypothesized that maintained firing of group III/IV muscle afferents after a fatiguing adductor pollicis (AP) contraction would decrease voluntary activation and force of AP and ipsilateral elbow flexors. In two experiments (n = 10) we examined voluntary activation of AP and elbow flexors by measuring changes in superimposed twitches evoked by ulnar nerve stimulation and transcranial magnetic stimulation of the motor cortex, respectively. Inflation of a sphygmomanometer cuff after a 2-min AP maximal voluntary contraction (MVC) blocked circulation of the hand for 2 min and maintained firing of group III/IV muscle afferents. After a 2-min AP MVC, maximal AP voluntary activation was lower with than without ischemia (56.2 ± 17.7% vs. 76.3 ± 14.6%; mean ± SD; P < 0.05) as was force (40.3 ± 12.8% vs. 57.1 ± 13.8% peak MVC; P < 0.05). Likewise, after a 2-min AP MVC, elbow flexion voluntary activation was lower with than without ischemia (88.3 ± 7.5% vs. 93.6 ± 3.9%; P < 0.05) as was torque (80.2 ± 4.6% vs. 86.6 ± 1.0% peak MVC; P < 0.05). Pain during ischemia was reported as Moderate to Very Strong. Postfatigue firing of group III/IV muscle afferents from the hand decreased voluntary drive and force of AP. Moreover, this effect decreased voluntary drive and torque of proximal unfatigued muscles, the elbow flexors. Fatigue-sensitive group III/IV muscle nociceptors act to limit voluntary drive not only to fatigued muscles but also to unfatigued muscles within the same limb.
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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 | 0.000 |
| 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) | 0.001 | 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".