In‐Home Remote Assessment of the <scp>MDS</scp>‐<scp>UPDRS</scp> Part <scp>III</scp>: Multi‐Cultural Development and Validation of a Guide for Patients
Bibliographic record
Abstract
BACKGROUND: The shift toward virtualized care introduces challenges in assessing the motor severity of Parkinson's disease (PD). The Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III, the most used rating scale in PD, lacks validation for synchronous remote administration. OBJECTIVE: Our goal was to validate the usability of a patient guide to allow an accurate video-based MDS-UDPRS part III remote examination. METHODS: We conducted a multi-stage mixed methods study that included a team consensus for the concept of the guide, cognitive pretesting, and usability (system usability scale, [SUS]) testing in five sites (total n = 25 participants) with distinct linguistic and cultural contexts. RESULTS: A multi-language (English, Portuguese, Spanish, and traditional Chinese) largely pictograph guide of the MDS-UPDRS part III remote examination reached benchmark for usability (SUS score ≥68) in 25 participants who completed the synchronous remote assessment. CONCLUSIONS: The MDS-UDPRS part III remote examination guide can be used remotely accurately, and facilitate clinical practice and research in a paradigm of telemedicine.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".