Fatigue effects on motor unit activity during submaximal contractions
Bibliographic record
Abstract
Conwit RA, Stashuk D, Suzuki H, Lynch N, Schrager M, Metter EJ. Fatigue effect on motor unit activity during submaximal contractions. Arch Phys Med Rehabil 2000;81:1211-6. Objective: To examine motor unit changes during the development of fatigue in healthy subjects. Design: Automated decomposition-enhanced spike-triggered averaging was used to characterize motor unit size and firing rate in the dominant vastus medialis during maintained contractions at 10% and 30% of maxima voluntary contraction (MVC). Setting: Academic outpatient neuromuscular clinic. Participants: Healthy laboratory personnel. Main Outcome Measures: Surface electromyogram, surface-detected motor unit action potential amplitude (S-MUAP), mean firing rate, force (MVC), motor unit index. Results: Surface electromyogram values and S-MUAP amplitudes increased during both 10% and 30% MVC fatiguing contractions, while mean firing rates decreased. A motor unit index, indicating the degree of motor unit pool activation, increased similarly to S-MUAP size, implying that new and larger units were recruited to maintain the contraction. Repeated contractions led to earlier motor unit changes and fatigue. Conclusion: During submaximal fatiguing contractions, additional motor units are activated to maintain strength. These changes begin early, within the first minute, particularly after a previous fatiguing effort. © 2000 by the American Congress of Rehabilitation Medicine and the American Academy of Physical Medicine and Rehabilitation
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".