Control of dynamic stability during gait termination on a slippery surface in Parkinson's disease
Bibliographic record
Abstract
Abstract This study investigated how Parkinson's disease (PD) affects the ability to switch from locomotion to gait termination (GT) during planned and cued GT and examined the effect of PD on the integration of a reactive, balance maintenance strategy into voluntary GT. After a series of stops on a stable surface, eight participants with and 10 without PD stopped on a surface, which slid quickly and unexpectedly forward mimicking a slippery surface. PD caused instability during the completely voluntary nonslippery stops ( P = 0.012) but not during the slippery stops, which required a reactive movement. The PD group walked slower [0.9–1.0 m/s vs. 1.3 m/s, respectively ( P < 0.001)] with shorter steps during the first step of nonslippery GT ( P = 0.016) and with wider steps during all steps of nonslippery GT ( P ≤ 0.05). Similar to controls, the PD group increased lateral stability during planned GT compared to cued GT ( P = 0.007). The timing of gait termination was similar between groups in all conditions. During the unexpected perturbation, both groups used a generalized slip response to regain balance after the perturbation. PD did not affect the ability to stop walking or to integrate a balance‐correcting response into GT but did affect movement speed, size, and stability of the voluntary movement. © 2008 Movement Disorder Society
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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 | 0.000 | 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) | 0.000 | 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".