Remotely Delivered Task-Oriented Training and Evaluation (reTOTE) for Stroke Rehabilitation
Bibliographic record
Abstract
Objective: To determine the effect of a multifaceted task-oriented training intervention delivered through telerehabilitation (Remote Task-Oriented Training and Evaluation [reTOTE]) on stroke survivors' activity, performance, quality of life, and confidence. Design: Cohort study with repeated measures at pre- and post-reTOTE intervention and 1-month follow-up. Setting: Virtual through telerehabilitation. Participants: Twelve (N=12) stroke survivors. Interventions: The reTOTE intervention was individualized for each participant during 8 sessions designed with evidence-based components of constraint-induced movement therapy, principles of experience-dependent plasticity, and use of the Cognitive Orientation to daily Occupational Performance approach. Main Outcome Measures: Motor Activity Log, Canadian Occupational Performance Measure, Stroke Impact Scale, and the Activities-specific Balance Confidence Scale. Results: The results demonstrated an increase in performance of functional abilities in stroke survivors' lives immediately and at 1-month follow-up after completing reTOTE compared with preintervention. Conclusions: This study indicates the importance of using an evidence-based, individualized, task-oriented therapeutic intervention for stroke survivors and the feasibility of remote delivery through telerehabilitation. Implementation of reTOTE may allow for access to rehabilitation that could improve meaningful therapeutic outcomes for stroke survivors.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".