Association Between Baseline Gait Parameters and Future Fall Risk in Patients With De Novo Parkinson’s Disease: Forward Versus Backward Gait
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Falls are not uncommon even in patients with early stages of Parkinson's disease (PD). The aims of this study were to determine the relationships between gait parameters and falls and identify crucial gait parameters for predicting future falls in patients with de novo PD. METHODS: We prospectively recruited patients with de novo PD, and evaluated their baseline demographics, global cognitive function on the Montreal Cognitive Assessment test, and parkinsonian motor symptoms including their subtypes. Both forward gait (FG) and backward gait (BG) were measured using the GAITRite system. The history of falls in consecutive patients with de novo PD was examined along with 1 year of follow-up data. RESULTS: Among the 76 patients with de novo PD finally included in the study, 16 (21.1%) were classified as fallers. Fallers had slower gait and shorter stride for FG and BG parameters than did non-fallers, while stride-time variability was greater in fallers but only for BG. Multivariable logistic regression analysis revealed that slow gait was an independent risk factor in BG. CONCLUSIONS: Among the patients with de novo PD, gait speed and stride length were more impaired for both FG and BG in fallers than in non-fallers. It was particularly notable that slow BG was significantly associated with future fall risk, indicating that BG speed is a potential biomarker for predicting future falls in patients with early-stage PD.
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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 | 0.000 | 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".