Differentiating gait behaviors between early-stage dementia with Lewy bodies and Parkinson’s disease
Bibliographic record
Abstract
INTRODUCTION: There is a need for improved biomarkers given that differentiating Dementia with Lewy bodies (DLB) from Parkinson's disease (PD) can be difficult in the earlier stages. Therefore, this study aimed to characterize and evaluate whether gait differences exist between PD and DLB patients in the early course of their disease by assessing normal and dual task walking conditions. METHODS: Twenty-six PD and 20 DLB patients who were within five years of their initial diagnosis and 16 healthy older adults walked across a 6-meter pressure sensor walkway under three conditions (i) self-paced gait, (ii) walking while subtracting 1 s from 100 and (iii) walking while subtracting serial 7 s from 100. RESULTS: During self-paced gait, DLB patients demonstrated impaired pace (velocity, step length) and rhythm (stance time) compared to early-stage PD patients. Study findings revealed velocity, step length, step time, stance time and step velocity variability offered moderate accuracy for discriminating DLB from PD patients. Increasing cognitive load during dual tasking (serial 1 s vs. serial 7 s) did not expose or intensify any gait differences between PD and DLB. CONCLUSION: These findings suggest quantitative gait measurements may be a promising, sensitive, and selective biomarker for differentiating clinical patterns of neurodegeneration. An understanding of early disease gait profiles may also prove useful as a tool for tracking phenoconversion to a synucleinopathy in those prodromal patients who are at high risk, such as those with isolated Rapid Eye Movement Sleep Behavior Disorder.
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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 | 0.001 |
| 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".