Therapists' perspectives on a new portable hand telerehabilitation platform for home-based personalized treatment of stroke patients.
Bibliographic record
Abstract
OBJECTIVE: Patients who have sustained a stroke suffer from residual motor impairments. Stroke can limit their ability to employ their impaired upper limb properly. Hand function is particularly one of the most frequently persisting consequences of stroke. This paper introduces a new portable hand telerehabilitation platform (PHTP) for home-based personalized treatment of stroke patients. The aims of this study are (1) to document the iterative design and development process of the PHTP, and (2) to explore the therapists' perspectives on implementing home-based treatment of stroke patients. MATERIALS AND METHODS: Local therapists were involved early in designing and developing the PHTP. We collected views of 84 therapists practicing in North America via an online survey. RESULTS: Therapists' perspectives on the introduced prototype support the use of the PHTP to provide home-based telerehabilitation for stroke patients. The System Usability Scale score was 70 for the PHTP, indicating that the platform is usable. The rest of the qualitative results obtained from custom questionnaires showed consistency in the platform design, high perceived usability and good acceptability among the therapists' community. CONCLUSIONS: In sum, the results encourage and support fine-tuning of the PHTP, commercializing it, and conducting prospective clinical studies.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".