Detecting cognitive impairment in cerebrovascular disease using gait, dual tasks, and machine learning
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment is common after a stroke, but it can often go undetected. In this study, we investigated whether using gait and dual tasks could help detect cognitive impairment after stroke. METHODS: We analyzed gait and neuropsychological data from 47 participants who were part of the Ontario Neurodegenerative Disease Research Initiative. Based on neuropsychological criteria, participants were categorized as impaired (n = 29) or cognitively normal (n = 18). Nested cross-validation was used for model training, hyperparameter tuning, and evaluation. Grid search with cross-validation was used to optimize the hyperparameters of a set of feature selectors and classifiers. Different gait tests were assessed separately. RESULTS: The best classification performance was achieved using a comprehensive set of gait metrics, measured by the electronic walkway, that included dual-task costs while performing subtractions by ones. Using a Support Vector Machine (SVM), we could achieve a sensitivity of 96.6%, and a specificity of 61.1%. An optimized threshold of 27 in the Montreal Cognitive Assessment (MoCA) revealed lower classification performance than the gait metrics, although differences in classification results were not significant. Combining the classifications provided by MoCA with those provided by gait metrics in a majority voting approach resulted in a higher specificity of 72.2%, and a high sensitivity of 93.1%. CONCLUSIONS: Our results suggest that gait analysis can be a useful tool for detecting cognitive impairment in patients with cerebrovascular disease, serving as a suitable alternative or complement to MoCA in the screening for cognitive impairment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".