Validation of the Portuguese <scp>MDS</scp>‐<scp>UPDRS</scp>: Challenges to Obtain a Scale Applicable to Different Linguistic Cultures
Bibliographic record
Abstract
BACKGROUND: The MDS-UPDRS has been available in English since 2008, showing satisfactory clinimetric results and being proposed as the new official benchmark scale for Parkinson's disease (PD), being cited as a core instrument for PD in the National Institutes of Neurological Disorders and Stroke Common Data Elements program. For this reason, the MDS created guidelines for development of MDS-UPDRS official, clinimetrically validated translations. OBJECTIVE: This study presents the formal process used to obtain the officially approved Portuguese version of the MDS-UPDRS. METHODS: The study consisted of three phases: (1) Independent translation by Portuguese and Brazilian teams followed by a challenging consensus process that this article particularly emphasizes; (2) Cognitive pretest involving raters and patients from both Portugal and Brazil; (3) Validation test with a sample of 367 native Portuguese-speaking PD patients. RESULTS: The overall factor structure of the Portuguese version was consistent with the English version based on a comparative fit index ≥0.96 for all four parts of the MDS-UPDRS. CONCLUSION: This version can be designated as the official Portuguese version of the MDS-UPDRS.
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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 | 0.074 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".