‘Muscular wisdom’ revisited: Decaying rates of stimulation mitigate torque loss
Bibliographic record
Abstract
During a sustained high-intensity isometric maximal voluntary contraction (MVC), declining motor unit firing rates (MUFRs) accompany torque loss. This decline (∼50% over 60 s) helps to maintain torque by preserving peripheral electrical propagation and matching the slowing contractile properties with torque loss (i.e., 'muscular wisdom'). However, it has been suggested that reduced MUFRs contribute to torque loss. Here, we compared torque loss between constant and decaying rates of electrical stimulation to mimic MUFRs reported during MVCs. The dorsiflexors of 8 males and 5 females (21-30 years) underwent three 60 s muscle fatiguing conditions: (1) sustained MVC; (2) constant high-frequency electrical stimulation (40 Hz); and (3) exponentially decaying stimulation rate (from 40 to 20 Hz). The decaying rate demonstrated less torque loss compared with the sustained high-frequency stimulation and the MVC conditions (P < 0.01). Furthermore, torque increased (by ∼17%, P < 0.005) when the constant high-frequency condition was switched to 20 Hz for 2 s at task termination. Conversely, torque loss was accelerated when the decaying stimulation rate was switched from 20 to 40 Hz for 2 s at task termination (by ∼16%, P < 0.001). Following all conditions, evoked twitch responses slowed (by 29%-77%, P < 0.01) but M-wave amplitude was reduced only for the constant high-frequency condition (by ∼23%, P < 0.01). Thus, the reduction in stimulation rates maintained optimal activation by matching the fatigue-induced contractile slowing in combination with preserved peripheral electrical conductance. Therefore, reducing the activation rate preserves torque, rather than contributing to torque loss during high-intensity contractions, thereby supporting the muscle wisdom hypothesis.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".