Comparison Between Tests of Fatigue and Force for Trunk Flexion
Bibliographic record
Abstract
STUDY DESIGN: A quasi-experimental study was conducted. OBJECTIVE: To compare intraindividual differences between trunk flexion isometric force, fatigue during isometric contraction (isometric fatigue), and fatigue during sit-ups (dynamic fatigue). SUMMARY OF BACKGROUND DATA: Trunk flexion force and trunk flexion fatigue commonly are used to assess trunk flexion fitness. The use of one test is appropriate only if the tests are highly correlated. METHODS: This study measured force and either isometric fatigue (time to task failure, n = 79) or dynamic fatigue (number of sit-ups in 2 minutes, n = 73) in self-selected, healthy secondary school (I-III) subjects. In 15 subjects, both isometric and dynamic fatigue were measured. RESULTS: After control was used for upper body mass, the R2(adj) was 0.34 between isometric force and isometric fatigue, 0.31 between isometric force and dynamic fatigue was, and 0.27 between dynamic fatigue and isometric fatigue. Including gender in the model did not affect the results. CONCLUSIONS: The lack of a high correlation between trunk flexion isometric fatigue, dynamic fatigue, and isometric force suggests that health care professionals should be careful to select the test that answers their particular needs or question.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".