Axial and appendicular postural abnormalities and associations with balance, gait and physical function in individuals with Parkinson's disease
Bibliographic record
Abstract
Background: Individuals with Parkinson disease (PD) may have a flexed posture, but only axial postural abnormalities (PAs) are generally investigated. Purpose: The objective was to verify if PAs of the axial and appendicular skeleton observed in PD occur in an interrelated manner to maintain balance and physical function. Methods: A cross-sectional observational study. Sixty-nine individuals with PD were evaluated by computerized photogrammetry. The MDS-UPDRS scale was used to analyze the physical function and the Mini-BESTest to assess balance. To determine the relationship between PAs and clinical aspects, multiple linear regression analysis was performed, setting age and levodopa equivalent dose as covariates. Results: The anterior trunk inclination angles were significantly correlated with the flexion angles of the elbows, hips and knees (p˂0.01). Larger head flexion was correlated with worsening physical function (p=0.013) and gait (p=0.043); greater trunk, hip and knee flexion were correlated with reduced postural instability (p˂0.05), and greater knee flexion was correlated with improvements in gait deficits (p=0.013). Conclusion: Postural abnormalities in the axial and appendicular joints of people with PD appear to occur in an organized and interrelated manner as a body compensation used to improve physical function and reduce balance and gait deficits.
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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 | 0.001 |
| 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".