Is freezing of gait correlated with postural control in patients with moderate‐to‐severe Parkinson’s disease?
Bibliographic record
Abstract
Freezing of gait (FoG) is one of the main reasons for movement initiation disorders and abnormal coupling of posture and gait in Parkinson's disease (PD). Patients with FoG have poor postural control when compared to patients without FoG. However, the nature of the interrelationship between FoG and domains of postural control remains unknown. The aim of this study was to estimate the association between different domains of postural control and severity of FoG in patients with moderate-to-severe PD. Thirty patients with idiopathic PD with FoG (age range 45-80 years, Hoehn & Yahr stages 3 and 4) participated in the study. We evaluated objective (FoG-ratio during turning task) and subjective (New Freezing of Gait Questionnaire, NFoG-Q) measures of FoG severity, reactive postural adjustments in response to an external perturbation, first step anticipatory adjustment for step initiation and quiet standing stability. In the multiple regression analysis, step initiation was the strongest significant correlation of the NFoG-Q score explaining 23% of the variance of the assessment. For the objective FoG measure, mediolateral CoP amplitude in quiet standing and mediolateral CoP amplitude in step initiation explained 39% of the variance of the FoG-ratio. As main conclusions, this study identified the association between objective and subjective measure for FoG severity and postural control domains. The results support conducting step initiation training during rehabilitation of individuals with FoG.
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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 | 0.001 | 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".