Motor and non-motor predictors of freezing of gait in Parkinson's disease: A retrospective cohort study
Bibliographic record
Abstract
INTRODUCTION: Freezing of gait (FOG) is a debilitating episodic gait disorder that significantly reduces the quality of life (QoL) in patients with Parkinson's disease (PD). Diagnosing and treating FOG remains a major medical challenge. This study aimed to assess the correlation between FOG and both motor and non-motor clinical characteristics in patients with PD. METHODS: In this retrospective cohort study, 112 patients with PD were divided into two groups using the New Freezing of Gait Questionnaire (NFOG-Q): one group with FOG (PD-FOG, 53 patients) and one group without FOG (PD-nFOG, 59 patients). The severity of PD and FOG was assessed using the Unified Parkinson's Disease Rating Scale (UPDRS), the Hoehn-Yahr (H-Y) staging system, and the NFOG-Q. The study also analyzed non-motor symptoms, including sleep disturbances, cognitive impairments, depression, anxiety, apathy, fatigue, and QoL. RESULT: The prevalence of FOG was 47.3%. The PD-FOG group exhibited a longer duration of PD (P = 0.002), a higher H-Y stage indicating PD progression (P = 0.003), and elevated anxiety levels (P = 0.003) compared to the PD-nFOG group. According to binary logistic regression analysis, the higher H-Y stage (P = 0.022), anxiety level (P = 0.005), UPDRS part II (P = 0.001), and part III (P = 0.008) were significant predictors for the occurrence of FOG. CONCLUSION: Patients with Parkinson's disease who have a higher Hoehn-Yahr (H-Y) stage, higher UPDRS score, and elevated levels of anxiety are more likely to experience 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".