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Record W4310954178 · doi:10.1002/mds.29279

It Is as It Was: MDS‐UPDRS Part III Scores Cannot Be Combined with Other Parts to Give a Valid Sum

2022· article· en· W4310954178 on OpenAlexaff
Christopher G. Goetz, Dongrak Choi, Yuanyuan Guo, Glenn T. Stebbins, Tiago Mestre, Sheng Luo

Bibliographic record

VenueMovement Disorders · 2022
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNational Institute on AgingInternational Parkinson and Movement Disorder Society
KeywordsRating scalePsychologySet (abstract data type)Physical medicine and rehabilitationScale (ratio)Physical therapyMovement disordersDiseaseMedicineComputer scienceDevelopmental psychology

Abstract

fetched live from OpenAlex

BACKGROUND: Original clinimetric analyses by the Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) developers did not confirm the validity of summing the scores of its parts. Recent studies used the summed score of Part III and other parts as efficacy outcomes. OBJECTIVE: The aim of this study was to establish whether summing scores of MDS-UPDRS parts can be recommended. METHODS: Using 7466 full MDS-UPDRS scores, we applied two-step factor analysis as in the original article to reassess the validity analysis with the threshold criterion set at comparative fit index ≥0.9. RESULTS: All comparative fit indexes of any combination including Part III were lower than 0.90. CONCLUSIONS: Summing Part III MDS-UPDRS scores with other parts is not clinimetrically sound. The MDS-UPDRS is a validated four-part scale with corresponding individual part scores and needs to be used within the limits originally presented. © 2022 International Parkinson and Movement Disorder Society.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.104
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.956
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0440.104
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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.

Opus teacher head0.019
GPT teacher head0.270
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
GenreEmpirical

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".

Quick stats

Citations21
Published2022
Admission routes1
Has abstractyes

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