Conflicting and non-conflicting visual cues lead to error in gait initiation and gait inhibition in individuals with freezing of gait
Bibliographic record
Abstract
INTRODUCTION: We asked whether conflicting visual cues influences gait initiation, gait inhibition and postural control in Parkinson's disease (PD) between freezers, non-freezers and healthy older adults. METHODS: Twenty-five PD participants on dopaminergic medication and 17 healthy older adults were asked to initiate or refrain gait depending on visual cues: green GO (GG), green STOP (GS), red GO (RG), red STOP (RS). Center of pressure (CoP) displacement, variability and mean velocity (VCoP) in the anterior-posterior (AP) and medial-lateral (ML) directions and movement time (MT) were measured. RESULTS: Gait initiation: Both freezers and non-freezers were different from controls in GG and GS. In GS, freezers had smaller CoP displacement and velocity in both directions (p<0.01), while non-freezers had smaller VCoP in AP and ML (p<0.01). AP CoP displacement in GS was smaller in freezers compared to non-freezers (p<0.05). Freezers had longer MT compared to controls in GG and compared to both groups in GS (p<0.01). Gait inhibition: Controls and freezers had larger CoP displacement variability (p<0.05) and velocity (p<0.01) in both directions in RG compared to RS. No differences were seen in non-freezers. Three freezers initiated walking during the RG or RS conditions. CONCLUSION: Freezers were in general slower at initiating gait, displayed a more restrictive postural strategy and were more affected by the conflicting conditions compared to both controls and non-freezers. In freezers, the conflicting visual cues may have increased the cognitive load enough to provoke delays in processing the visual information and implementing the appropriate motor program.
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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 | 0.000 | 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".