Dual-Task Gait Cost and Frontal Lobe Integrity in Poststroke: Results From ONDRI
Bibliographic record
Abstract
Abstract Dual-task gait performance is a marker of motor-cognitive interactions modulated by the frontal lobes. After a stroke, gait disturbances are more evident, particularly when concurrently completing a mental task and walking, an effect called high dual-task cost (DTC). Following a stroke, the potential association of high-DTC, integrity of the frontal lobes and cognitive functioning is unclear. This study screened 161 participants with stroke history from the Ontario Neurodegenerative Disease Research Initiative (ONDRI)-cerebrovascular disease cohort (69.2±7.41 years of age; 31.7% women). Individuals scoring zero in the National Institute of Health Stroke Scale were analyzed (n=102). DTC was the percentage change in gait speed from the single to dual-task condition. Standardized normal-appearing white matter (NAWM) and grey matter (NAGM) volumes from superior, middle and inferior frontal lobe were compared between DTC quartiles (adjusted for age, sex, and education) using a multivariate model (MANOVA), with total frontal lobe volume as a covariate. Another model compared group performance across 5 adjusted cognitive domains (attention, memory, language, visuospatial performance, and executive functioning). Univariate tests revealed that NAWM volume in the superior frontal lobe (F=4.50; p=0.005; partial eta-squared=0.122) was significantly different across DTC quartiles. Contrast tests suggested that the first quartile had larger NAWM than the second and fourth. DTC quartiles also showed differences in attention (F=2.93; p=0.03; partial eta-squared=0.083) and contrast tests indicated that the first quartile performed significantly better than second and fourth. DTC poststroke may be a proxy for structural integrity of superior frontal lobe regions and attention.
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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 | 0.002 |
| 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 | 0.000 |
| 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".