Dynamic Stability of the Trunk During Unstable Sitting in People With Low Back Pain
Bibliographic record
Abstract
STUDY DESIGN: Cross-sectional study. OBJECTIVE: To evaluate the dynamic stability and kinematics of the trunk during unstable sitting, and to determine the differences in these biomechanical parameters between healthy participants and participants with low back pain (LBP). SUMMARY OF BACKGROUND DATA: Patients with LBP exhibited alterations in trunk kinematics while performing different dynamic tasks and in static posture as a result of pain. It is not clear if changing in trunk motion may reduce postural control and the ability to perform a balancing task. METHODS: Twenty-three participants with LBP and 31 healthy participants were requested to sit on a custom-made swinging chair and to regain the balance after tilting the chair backward for 10° and 20°. Lumbar spine, pelvis, and chair motions were recorded using FASTRAK sensors. The thoracolumbar curvature of all participants was also evaluated in the standing position. The angular displacement of the chair was fitted in an equation describing the underdamped second-order response to a step input. RESULTS: Kinematic analysis showed that the hip range of motion increased whereas spine range of motion angle decreased in participants with LBP for both tilt angles (P < 0.05). There were no significant differences between the 2 subject groups in the time required to regain balance, and the natural frequency and damping ratio of the kinematic equation. Lumbar lordosis significantly decreased in LBP group. CONCLUSION: Participants with LBP showed trunk postural and movement adaptations that seems to be compensatory strategies to decrease the risk of further injuries and aggravation of the symptoms, but their ability to regain the balance was not affected by LBP. Clinicians should encourage patients with LBP to remain active while they are experiencing pain.
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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 | 0.001 |
| 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".