Age- and Parkinson-related differences in trunk and lower-limb muscle activation during four balance tasks: A case-control pilot study
Bibliographic record
Abstract
BACKGROUND: Poor postural control has been reported in people with Parkinson's disease, which could be explained by the changes in muscular activation patterns related to antigravitational muscles. This study aims to measure the muscle activation of antigravitational muscles during balance tasks in individuals, with and without Parkinson's. METHODS: Sixteen (16) participants (9 with Parkinson's), aged ≥65 yrs., performed 2 × 30-s trials of 4 balance tasks (bipodal and semi-tandem opened eyes and closed eyes) on a force platform (center of pressure measurement); while surface electromyography measurements were obtained bilaterally on the multifidus at L5, biceps femoris and medialis gastrocnemius. Electromyography amplitude analysis was processed by the Root Mean Square (250 ms window epochs) and normalized by the peak of activation during the balance task, to determine each muscle's activity level. FINDINGS: The Parkinson's group reported lower muscle activation than control across tasks (in mean for multifidus = 8%, biceps femoris = 16%, gastrocnemius = 7%), although not statistically significant. Parkinson's reported significantly poorer postural control than control, mainly for the center of pressure sway ellipse area (p = 0.016) from challenge balance tasks such as semi-tandem. INTERPRETATION: Poor postural control was confirmed in the Parkinson's group, but not significantly associated by the changes from muscle activation of trunk and lower limbs, during balance performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".