Application of the LaGrange Polynomial in Skeletal Muscle Fatigue Analysis
Bibliographic record
Abstract
The percentage of decrement in torque and the number of serial contractions are mutually exclusive methodological controls in the study of muscular fatigue. This paper examines the feasibility of using the LaGrange polynomial in the analysis of voluntary muscular fatigue patterns. Twenty-one men (ages 20-60 years) reported to the orthopedic biomechanics laboratory on 2 days separated by 4 months. During both sessions, participants completed three maximal isokinetic (180 deg x s(-1)) contractions of the knee extensors to serve as baseline, before starting the fatigue protocol. The fatigue protocol consisted of serial contractions until a 50% strength decrement was reached. The LaGrange polynomial was first used to interpolate the individual fatigue pattern for each participant into 15 data points (trials). Data analysis was then conducted on these 15 data points. Intraclass correlation analysis of variance showed that the reliability of baseline torque was very good (.93). Baseline torque, the average of three trials, exhibited a 5.4 Nm (6%) increase from the first to second test session (p < .05). The mean level of torque, average of the 15-point fatigue pattern, also increased 7.5 Nm (15%) on the second test session (p < .05). The classic torque deficit for the first trial of a fatigue series was preserved by the interpolation method. Serial contractions resulted in an average decrease in torque of 29.5 Nm (50%) from the first to last trial (p <.05). The interpolation method also retained the linear and quadratic trend components commonly observed for isometric and isokinetic fatigue patterns. The two trend components accounted for 94.7% of the total trial variance. It was concluded that the LaGrange polynomial used to interpolate fatigue patterns to fewer data points was successful.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".