Interaction Between Thoracic Movement and Lumbar Spine Muscle Activation Patterns in Young Adults Asymptomatic for Low Back Pain: A Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to investigate the interaction between thoracic movement and lumbar muscle co-contraction when the lumbar spine was held in a relatively neutral posture. METHODS: Thirty young adults, asymptomatic for back pain, performed 10 trials of upright standing, maximum trunk range of motion, and thoracic movement tasks while lumbar muscle activation was measured. Lumbar co-contraction was calculated, compared between tasks, and correlated to thoracic angles. RESULTS: Movement tasks typically exhibited greater co-contraction than upright standing. Co-contraction in the lumbar musculature was 67%, 45%, and 55% greater than upright standing for thoracic flex, thoracic bend, and thoracic twist, respectively. Generally, the thoracic movement task demonstrated greater co-contraction than the maximum task in the same direction. Co-contraction was also correlated to thoracic angles in each movement direction. CONCLUSION: Tasks with thoracic movement and a neutral lumbar spine posture resulted in increases in co-contraction within the lumbar musculature compared with quiet standing and maximum trunk range-of-motion tasks. Findings indicated an interaction between the 2 spine regions, suggesting that thoracic posture should be accounted for during the investigation of lumbar spine mechanics.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".