Spatio-Temporal Parameters and Range of Motion of Lower Limb Joints During Running in Individuals with Adolescent Idiopathic Scoliosis with Mild Thoracic and Lumbar Curvature
Bibliographic record
Abstract
Objective Adolescent Idiopathic Scoliosis (AIS) disrupts postural balance and movement control.The biomechanics of running in this population are not well understood.This study investigated spatiotemporal parameters and lower limb joint kinematics during running in AIS patients compared to healthy controls.Methods Fifteen female patients with AIS (right thoracic: 21.5±2.7°;and left lumbar: 23.1±1.6°),along with 15 healthy controls, participated in this study.Participants performed a running task at their self-selected speed while markers were attached at landmarks based on the Full-body lumbar spine model.An 8-camera Qualisys system and two Kistler force plates were used to capture the markers' spatial position and record the ground reaction forces, respectively.The data were digitized using Qualisys Track Manager (QTM), and the spatiotemporal and kinematic data were calculated using Visual3D software.MANOVA and Statistical Parametric Mapping tests were used to analyze between-group differences (p<0.05). ResultsThe AIS patients had lower height, body mass, and BMI (P < 0.05) than the control group.No significant differences were observed in spatiotemporal variables between the two groups.In the AIS patients, the abduction-adduction range of motion (ROM) on the right hip (p= 0.045) and the left knee (p= 0.058) were reduced, while inversion-eversion motion on the right ankle (p= 0.025) was greater in the AIS patients.Conclusion In AIS patients with mild thoracic and lumbar curvatures, the abductionadduction of the hip and knee, along with the inversion-eversion of the ankle, are altered.These changes may represent a neuromuscular adaptation aimed at optimizing balance during running.Rehabilitation that emphasizes strengthening the lower limb muscles is recommended for these patients.
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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) | 0.001 | 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".