Task-dependent neuromuscular adaptations in low back pain: a controlled experimental study
Bibliographic record
Abstract
Introduction This study investigated the variability in lumbar neuromuscular adaptations to pain, the task dependency of pain adaptations and the effect of these adaptations on motor performance. Methods Twenty-four healthy participants performed isometric back extension contractions at 45° and 90° trunk flexion under pain-free and experimental low back pain conditions induced by electrical stimulation. High-density surface electromyography recorded lumbar muscle activation strategies, and force steadiness was measured using a load cell. Results While considerable variability in neuromuscular adaptations to lumbar pain was observed among participants, consistent patterns were found between tasks. In the 90° trunk flexion position, both sides exhibited greater magnitudes of pain adaptations for muscle activity redistribution in the mediolateral axis (p < 0.05, 86% increase) and muscle activity amplitude (p < 0.001, 183% increase) compared to the 45° trunk flexion position. A significant negative correlation was found between the magnitude of the mediolateral spatial redistribution of muscle activity and force steadiness on the left side (p = 0.045). Discussion These findings highlight the intricate and task-dependent nature of neuromuscular adaptations to pain within lumbar muscles, and points toward a potential trade-off between pain adaptations and performance.
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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 | 0.003 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".