Anxiety Independently Contributes to Severity of Freezing of Gait in People With Parkinson’s Disease
Bibliographic record
Abstract
Freezing of gait is a disabling feature of Parkinson's disease, and it has been shown that nonmotor symptoms, such as anxiety and cognitive impairment, may be involved in the pathophysiology of the phenomenon. However, the association between freezing of gait severity and nonmotor symptoms is yet to be determined. Therefore, the overall aim of this study was to determine factors that contribute to severity of freezing of gait in people with Parkinson's disease. Participants (N=78) were assessed by disease-specific and self-report measures, including the Hospital Anxiety and Depression Scale (HADS), the Montreal Cognitive Assessment, and the Freezing of Gait Questionnaire (FOG-Q). Participants were classified as "freezers" if they scored ≥1 on item 3 of the FOG-Q; the sum of items 3-6 was used to determine freezing of gait severity. Freezers (N=27) showed higher scores on the HADS anxiety (p=0.002) and HADS depression (p=0.006) subscales. A multivariate linear model showed that disease severity (as measured by using the modified Hoehn and Yahr scale) accounted for 31% of the variance in FOG-Q severity scores (p<0.001). The presence of HADS anxiety ≥8 points increased the explained variance to 38% (p=0.010), and the full model (reached by adding the levodopa equivalent dose) explained 42% of the variance in freezing of gait severity (p=0.026). The findings provide additional support for the contribution of anxiety to greater freezing of gait severity, taking into account not only the frequency but the duration of the episodes, and suggest that anxiety should be routinely evaluated in people with Parkinson's disease who present with freezing of gait.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".