Pre- and unplanned walking turns in Parkinson’s disease – Effects of dopaminergic medication
Bibliographic record
Abstract
Although dopaminergic medication improves functional mobility in individuals with Parkinson's disease (PD), its effects on walking turns are uncertain. Our goals was to determine whether dopaminergic medication improves preplanned and unplanned walking turns in individuals with PD, compared to healthy controls. Nineteen older adults with mild-to-moderate PD and 17 healthy controls performed one of the following three tasks, presented randomly: walking straight, or walking and turning 180° to the right or left. The walking direction was visually cued before starting to walk (preplanned) or after (unplanned, i.e., 0.6m before reaching the turning point). Subjects with PD were assessed off dopaminergic medication (OFF) and on dopaminergic medication (ON) medication. Turning strategy (step and spin turns), turning performance (turning distance and body rotation) and walking pattern were analyzed for three turning steps. Irrespective of medication state and turning condition, step and spin turns followed a nearly 50:50 distribution. After intake of dopaminergic medication, subjects with PD increased their turning distance but not the amount of body rotation or their walking pattern. Compared to controls, turning impairments in subjects with PD remained while ON medication and problems regulating step width were the most prominent features of their walking pattern. Specifically, subjects with PD turned with narrower cross-over steps, i.e. when the external foot crossed over the line of progression of the internal leg. We conclude that turning impairments remained even after dopaminergic medication and problems modulating step width appears to be a critical feature for turning in PD.
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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) | 0.001 | 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".