Indices of Muscle Fatigue
Bibliographic record
Abstract
The objective of the study was to determine if any of the many indicators of localized muscle fatigue (LMF) mirrors the decline in force more closely (gold standard). If not, can a group of indicators can predict LMF better? Nine normal young subjects were required to exert their maximal voluntary contraction (MVC) and 40% of MVC in elbow flexion as long as they could. The magnitude of the force, EMG amplitude, median frequency (MF), muscle bed blood volume, and muscle oxygenation were measured for MVC. For the 40% MVC contraction in addition to the foregoing variables oxygen uptake (V0 2 ), ventilation volume and heart rate were also measured. The rate of perceived exertion (RPE), visual analog score (VAS) and body part discomfort rating (BPDR) were measured for both contractions. Data were subjected to the analysis of variance (ANOVA) with repeated measures, correlation and regression analysis. Different percentiles of the tasks were significantly different in both contractions (p<0.001). The MF was the strongest indicator of the force decline in MVC (r = 0.91; p<0.001) but in 40% MVC the VAS was a better indicated. None of the variables consistently represented LMF in different levels of contraction. A different grouping of objective and subjective measures for MVC and 40% MVC increased the predictability of the force decline (LMF).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".