Impact of Cognitive Impairments on Functional Ambulation in Stroke Patients
Bibliographic record
Abstract
Background: Regaining the ability to walk independently is the most important functional goal in rehabilitation of stroke patients. Cognitive impairments are increasingly recognized as affecting functional outcome in stroke. The purpose of this study was to determine the association between cognition and functional ambulation level in chronic stroke patients. Methods: Design: Cross-sectional, observational Setting: Tertiary care centre, Mumbai, India Participants: 60 ambulatory post-acute stroke patients Main outcome measures: • Cognition was assessed using Montreal Cognitive Assessment (MoCA) scale • Functional ambulation level was determined using Modified Hoffer functional ambulation classification (FAC). Results: The prevalence of cognitive impairments was 46%. According to FAC, 28.3% of the patients were community walkers. MoCA score discriminated between unlimited household and most limited community walkers (p<0.03) and also between least limited community and community walkers (p<0.04). Conclusion: Community ambulation is significantly limited in chronic stroke patients. Cognitive impairments are prevalent and persistent even after the acute phase. Cognition is an important factor in the attainment of community ambulation in chronic stroke patients. Along with physical impairments, cognitive impairments need to be specifically addressed for successful rehabilitation outcome in stroke.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".