Gait speed after mild stroke/transient ischemic attack was associated with long‐term adverse outcomes: A cohort study
Bibliographic record
Abstract
OBJECTIVE: The association between gait speed and adverse outcomes after stroke has not been fully illustrated. This study aimed to explore the association of gait speed on long-term outcomes in minor stroke or transient ischemic attack (TIA). METHODS: We performed a longitudinal study with acute minor stroke or TIA based on a subgroup of the Third China National Stroke Registry data. The gait speed was evaluated using a 10-meter walking test at discharge and 3 months after the stroke onset. The primary outcomes were poor functional outcomes at 1 year, defined by a modified Rankin Score (mRS) of 2-6. Additional outcomes included all-cause death, ambulate dependency (mRS score 4-6), cognitive impairment (Montreal Cognitive Assessment <26), stroke recurrence, and composite vascular events. RESULTS: The study sample included a total of 1542 stroke patients with a median age of 60 (53-68). At 1-year follow-up, 140 (9.20%) patients experienced poor functional outcomes. Faster gait speed at discharge was associated with lower incidence of poor functional outcome (OR = 0.89; 95% CI, 0.84-0.94), cognitive impairment (OR = 0.93; 95% CI, 0.89-0.96), ischemic stroke recurrence (HR = 0.92; 95% CI, 0.87-0.98), and composite vascular events (HR =0.94; 95% CI, 0.89-0.99) at 1 year. Faster gait speed at 3 months was associated with lower incidence of poor functional outcome (OR = 0.90; 95% CI, 0.85-0.95), ambulate dependency (OR = 0.86; 95% CI, 0.77-0.97), and cognitive impairment (OR = 0.92; 95% CI, 0.88-0.95) at 1 year. INTERPRETATION: Our findings indicated that slow gait speed after minor stroke or TIA may be an independent predictor for long-term poor outcomes. Gait speed may be considered as a vital sign during follow-up in post-stroke patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".