Degradation of postural control with aging
Bibliographic record
Abstract
Aging negatively impacts the ability to maintain postural stability due to degraded control systems. The entropic half-life, a non-linear variable that quantifies the transition of sample entropy with increasing time scales, quantifies the time that elapses before old positional information no longer influences, or is no longer related to, the control mechanisms that regulate the movement at the current center of pressure location. The entropic half-life provides a more representative and comprehendible way of detecting changes in complexity using measurement units of time. The purpose of this study was to determine the effects of aging on the magnitude and temporal structure of the center of pressure movement during quiet single-limb stance. Center of pressure data of 24 older and 24 younger subjects were analyzed. The complexity of the temporal structure of the center of pressure signal was quantified by calculating the entropic half-life of the center of pressure in the medio-lateral and anterior-posterior directions. The magnitude of movement was quantified using excursion of the center of pressure in the medio-lateral and anterior-posterior directions, the path length, and the 95% ellipse area of the center of pressure. The older subjects demonstrated a significantly shorter entropic half-life for the center of pressure in the anterior-posterior direction (p < 0.001), longer excursions of the center of pressure in the medio-lateral (p < 0.001) and anterior-posterior (p = 0.001) directions, increased center of pressure path lengths (p < 0.001), and increased 95% ellipse areas of the center of pressure (p < 0.001). The results from this study showed that even though older subjects demonstrated more frequent postural adjustments (shorter entropic half-life), this did not help to reduce the magnitude of movement of their center of pressure during quiet stance, thus indicating an impaired peripheral and/or central neuromuscular control mechanism.
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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 | 0.002 |
| 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".