Can training to dissociate trunk and pelvic motion influence thorax-pelvis coordination and lumbar spine dynamic stability?
Bibliographic record
Abstract
BACKGROUND: The large number of articulating joints within the spinal column provides an abundance of options to control its movement. However, the ability of individuals to consciously manipulate these movement options is poorly understood. OBJECTIVES: To determine if short-term training can improve the ability to consciously dissociate motion between the pelvis and thorax during repetitive pelvic tilting movements. DESIGN: Cross-over design with young healthy individuals. METHOD: Seventeen participants performed trials consisting of 35 continuous lift/lowers followed by 35 continuous anterior/posterior pelvic tilts while spine kinematics were recorded. Participants then underwent a 20-min training protocol designed to improve the control of pelvic motion and in particular the dissociation of pelvic and trunk motion. Post-training, the continuous pelvic tilt and lift/lower trials were repeated. Thorax-pelvis movement coordination was analyzed via vector coding and lumbar spine local dynamic stability was analyzed via Lyapunov exponents. Participants were grouped as being either high or low skill movers based on their ability to perform the pre-training pelvic tilt movements. RESULTS: The low skill movement group demonstrated statistically significant increases in the time spent using in-phase pelvic dominant (p = 0.028) and anti-phase pelvic dominant (p = 0.043) coordination patterns during the pelvic tilt movements after the completion of the training protocol. The high skill movement group showed no differences in their movement patterns post-training. CONCLUSIONS: Short-term training, targeted to improve the ability to dissociate pelvic from thorax motion, had a beneficial effect on the group of individuals who initially lacked skill performing the pelvic tilting task.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".