Quantitative Analysis of Dual-Task Rehabilitation
Bibliographic record
Abstract
BACKGROUND Dual-task impairment severely limits functional recovery post-stroke. This diagnostic accuracy study aimed to develop an eye-tracking-based system for objectively quantifying ankle-cognitive integration deficits in stroke survivors. METHODS This diagnostic accuracy study was conducted from January 2022 to October 2023 at Tsinghua Changgung Hospital in Beijing. A total of 20 healthy adults (mean age 53.15±6.26 years) participated in the study. In addition, 30 patients with a history of stroke (mean age 64.13±8.16 years, 8 females, disease duration 9.12±6.60 weeks) participated in a standardised dual-task evaluation. The novel system utilised 17 parameters, encompassing ankle kinematics (range of motion, velocity) and eye tracking (gaze duration, sweep latency), which were measured simultaneously during the cognitive motor task. Reliability was assessed by intragroup correlation coefficients (ICC), while criterion validity was assessed using 12 clinical evaluation metrics, including Spearman correlation with Montreal Cognitive Assessment (MOCA) scores and dual-task cost (DTC) percentages. RESULTS The system demonstrated that 88.2% of the evaluation parameters exhibited high consistency, with 55.8% showing a moderate correlation with clinical benchmark scales (p<0.05). Notably, MOCA, DTC%, and TUG-subtraction task duration were identified as key indicators of dual-task ability (P<0.05), while the Self-Rating Anxiety Scale showed lower sensitivity. Furthermore, ankle motion parameters exhibited a strong correlation with balance and fall risk (P<0.05), effectively serving as predictors of motor function recovery and fall risk in stroke patients. CONCLUSIONS This multimodal system reliably quantifies post-stroke dual-task deficits, with ankle kinematics and eye-tracking metrics serving as sensitive biomarkers for balance recovery and fall risk stratification. Findings advocate integrating objective dual-task metrics into neurorehabilitation protocols to optimize functional outcomes. (ChiCTR2300067640; URL: https://www.chictr.org.cn/showproj.html?proj=188211 ).
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.002 | 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".