Targeting coordination: How focus of attention influences spinal movement control in low back pain
Bibliographic record
Abstract
BACKGROUND: People with low back pain often demonstrate movement pattern and motor control differences compared to asymptomatic individuals. These differences can include both high and low levels of lumbar coordination variability. The ability to modulate coordination variability could be helpful in rehabilitation settings, but specific methods need to be investigated. METHODS: This study used marker-based motion capture and a modified vector coding procedure to assess lumbar coordination variability. 25 asymptomatic controls and 15 individuals with low back pain performed three trials (baseline, visual cue, ball) each consisting of 15 standing body-weight squats. The visual cue trial included instructions to focus on a small visual cue on the wall while performing squats. The ball trial required participants to perform squats while simultaneously focusing on throwing a small ball back and forth between the right and left hands without dropping it. FINDINGS: Lumbar coordination variability was not significantly different between the control and low back pain groups, and not significantly different between the baseline (15.3° ± 0.7°) and visual cue (15.3° ± 0.7°) trials. There was a significant increase during the ball (18.9° ± 1.0°; p < .001) trial compared to baseline. While most participants displayed greater variability during the ball trial than at baseline, a few individuals with low back pain and high baseline variability levels displayed decreased variability during the ball trial. INTERPRETATION: Throwing a ball while performing squats can immediately increase lumbar coordination variability, but fixing the gaze on a visual cue does not significantly change lumbar coordination variability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".