Clinical progression of Parkinson’s disease in the early 21st century: Insights from AMP-PD dataset
Bibliographic record
Abstract
Abstract Background Parkinson’s disease (PD) therapeutic strategies have evolved since the introduction of levodopa in the 1960s, but there is limited data on their impact on disease progression markers. Objective Delineate the current landscape of PD progression at tertiary subspecialty care and research centers. Method Using Accelerating Medicine Partnership-PD (AMP-PD) data harmonized from seven biomarker discovery studies (2010-2020), we extracted: overall [Schwab and England (S&E), PD Questionnaire (PDQ-39)]; motor [Movement Disorders Society Unified PD Rating Scale (MDS-UPDRS)-II and -III and Hoehn & Yahr (HY)]; and non-motor [MDS-UPDRS-I, University of Pennsylvania Smell Identification Test (UPSIT), Montreal Cognitive Assessment (MoCA), and Epworth Sleepiness Scale (ESS)] scores. Age at diagnosis was set as 0 years, and data were tracked for 15 subsequent years. Results Subjects’ (3,001 PD cases: 2,838 white, 1,843 males) mean age at diagnosis was 60.2±10.3 years and disease duration was 9.9±6.0 years at the baseline evaluation. Participants largely reported independence (S&E, 5y : 86.6±12.3; 10y : 78.9±19.3; 15y : 78.5±17.0) and good quality of life (PDQ-39, 5y : 15.5±12.3; 10y : 22.1±15.8; 15y : 24.3±14.4). Motor scores displayed a linear progression, whereas non-motor scores plateaued ∼10-15 years. Younger onset age correlated with slower overall (S&E), motor (MDS-UPDRS-III), and non-motor (UPSIT/MoCA) progression, and females had better overall motor (MDS-UPDRS-II-III) and non-motor (UPSIT) scores than males. Conclusions Twenty-first century PD patients remain largely independent in the first decade of disease. Female and young age of diagnosis were associated with better clinical outcomes. There are data gaps for non-whites and metrics that gauge non-motor progression for >10 years after diagnosis.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".