Isometric Force Parameters and Trunk Muscle Recruitment Strategies in a Population With Low Back Pain
Bibliographic record
Abstract
OBJECTIVE: This study correlates changes in trunk isometric force parameters and trunk muscle recruitment strategies in subjects with low back pain (LBP) and healthy participants. METHODS: A control group study with repeated measures was performed. Study participants included 15 control subjects and 14 patients with LBP. Participants were required to exert 50% and 75% of their maximal trunk flexion and extension. In a learning phase, feedback was provided, after which study participants were asked to perform 10 trials without any feedback. Spatiotemporal parameters of muscular activity and force production were recorded. Dependent variables included time to peak force, peak force variability, absolute error in peak force, electromyogram (EMG) burst duration for agonist muscles, and normalized integrated EMG. RESULTS: Average time to peak force was significantly longer for subjects with LBP than for healthy subjects. Subjects with LBP showed longer burst duration for all 4 muscles recorded. No group difference was noted in normalized integrated EMG. CONCLUSIONS: We suggest that the observed changes in trunk motor control and trunk muscle recruitment strategies are not only mediated by a neurophysiologic adaptation to chronic pain but also by cognitive adaptations modulated by fear of movement and fear of reinjury.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".