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Validation of a Patient-Reported Outcome Measure Sensitive to Diet and Nutraceutical Exposure in Parkinson’s Disease

2025· preprint· W7117547923 on OpenAlexaboutno aff
Laurie K. Mischley, Magdalena Murawska

Bibliographic record

VenuePreprints.org · 2025
Typepreprint
Language
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMinimal clinically important differenceRating scaleConvergent validityReliability (semiconductor)DiseaseConfirmatory factor analysisCohortPsychometricsScale (ratio)

Abstract

fetched live from OpenAlex

Background: The Patient-Reported Outcomes in Parkinson’s Disease (PRO-PD) scale is a 35-item visual analog measure designed to quantify symptom severity across motor and non-motor domains. Developed as a continuous, patient-centered outcome, PRO-PD captures patient-perceived change over time and is suitable for remote longitudinal assessment. This study evaluated the psychometric properties of PRO-PD across two independent datasets, in-cluding reliability, validity, factor structure, and minimal clinically important change (MCIC), and assessed its relevance to nutrition- and lifestyle-focused research. Methods: Convergent validity was evaluated in a cross-sectional clinical dataset (n = 46) using established clinician-rated and patient-reported instruments, including Hoehn and Yahr, Unified Parkinson’s Disease Rating Scale (UPDRS), Parkinson’s Disease Questionnaire-39 (PDQ-39), Montreal Cognitive Assessment (MoCA), and PROMIS measures. Internal con-sistency, temporal stability, factor structure, and known-groups validity were assessed in a large remote-monitoring cohort (n = 2,612). MCID thresholds were estimated in a longitudinal sub-sample (n = 390) using anchor-based methods, multinomial regression, and receiver operating characteristic analyses. Joint modeling (two-stage procedure) approach was applied to model the sensitivity of PRO-PD change in time to the binary diet factors evolution in time. Results: PRO-PD demonstrated strong convergent validity with established clinical measures, excellent internal consistency (Cronbach’s α = 0.93–0.95), and good test–retest reliability (ICC = 0.78 overall; 0.89 at 6 months). Confirmatory testing of a previously proposed eight-factor structure showed suboptimal fit, leading to a parsimonious four-factor solution (Cognitive, Autonomic, Motor, Psycho-Emotional) explaining 47.6% of variance. PRO-PD scores increased significantly with advancing disease duration and stage. MCID thresholds were +53.5 points for worsening and −78.5 points for improvement (AUC = 0.63–0.71), with greater sensitivity for detecting deterioration than improvement. Joint modeling further demonstrated sensitivity of PRO-PD to baseline dietary behaviors and dietary change over time. Conclusions: These findings support PRO-PD as a psychometrically robust, low-burden out-come measure suitable for remote monitoring and for use across nutrition, nutraceutical, and pharmacologic intervention studies, including in early PD where traditional scales may lack sensitivity.

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)0.025
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.036
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.098
GPT teacher head0.353
Teacher spread0.255 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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