Freezing of gait in Parkinson’s disease with glucocerebrosidase mutations: prevalence, clinical correlates and effect on quality of life
Bibliographic record
Abstract
Objectives Mutations in glucocerebrosidase (GBA1) can change the clinical phenotype of Parkinson’s disease (PD). This study aimed to explore the clinical characteristics of freezing of gait (FOG) in PD patients with GBA1 mutations. Methods A whole-exome sequencing analysis was used to identify the GBA1 mutations (pathogenic or likely pathogenic) and exclude other PD-related gene mutations. A forward binary logistic regression model was conducted to identify the associated factors of FOG. The stepwise multiple linear regression analysis models were used to explore the effect of FOG on quality of life. Results The prevalence of FOG in patients with GBA1 mutations (30/95, 31.6%) was significantly higher than those in patients without GBA1 mutations (152/760, 20%) (p = 0.009). A higher (i.e., worse) Unified PD Rating Scale part III score (OR = 1.126, 95%CI = 1.061–1.194, p < 0.001) and a lower (i.e., worse) Montreal Cognitive Assessment score (OR = 0.830, 95%CI = 0.713–0.967, p = 0.017) were significantly associated with FOG in PD patients with GBA1 mutations. The presence of FOG was significantly associated with the decreased (i.e., worse) score of PD Questionnaire 39 after adjustment for sex, age, disease duration, motor score, and non-motor score (B = 14.981, p = 0.001). Conclusion FOG is a relatively common disabling symptom in PD patients with GBA1 mutations, which is affected by motor disability and cognitive decline. Quality of life is reduced in patients with FOG and GBA1 mutations.
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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 | 0.001 | 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 | 0.000 | 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".