Does Poor Gait Performance Predict Risk of Developing Dementia? Results From a Meta-analysis (P2.244)
Bibliographic record
Abstract
Objective: The aim of this meta-analysis was to systematically examine the association of poor gait performance with incidence of dementia. Background: Poor gait performance predicts risk of developing dementia, but no structured critical evaluation has been studied this association yet. Methods: An English and French Medline search was conducted in June 2015, with no limit of date, using the Medical Subject Headings terms "Gait" OR "Gait Disorders, Neurologic" OR "Gait Apraxia" OR "Gait Ataxia" AND "Dementia" OR "Frontotemporal Dementia" OR "Dementia, Multi-Infarct" OR "Dementia, Vascular" OR "Alzheimer Disease" OR "Lewy Body Disease" OR "Frontotemporal Dementia With Motor Neuron Disease" [Supplementary Concept]. Poor gait performance was defined by standardized tests of walking, and dementia was diagnosed according to international consensus criteria. Four etiologies of dementia were identified: any dementia, Alzheimer disease (AD), vascular dementia (VaD), and non-AD (i.e., pooling VaD,mixed dementias, and other dementias). Fixed effects meta-analyses were performed on the estimates in order to generate summary values. Results: Of the 796 identified abstracts, 12 (1.5[percnt]) were included in this systematic review and meta-analysis. Poor gait performance predicted dementia (pooled Hazard Ratio [HR] combined with Relative Risk and Odds ratio=1.53 with P<0.001 for any dementia, pooled HR=1.79 with P<0.001 for VaD, HR=1.89 with P-value <0.001 for non-AD). Findings were weaker for predicting AD (HR=1.03 with P-value=0.004). Conclusions: This meta-analysis provides evidence that poor gait performance predicts dementia. This association depends on the type of dementia: poor gait performance is a stronger predictor of non-AD dementias than AD.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.068 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".