Longitudinal Monitoring of Parkinson's Disease in Different Ethnic Cohorts: The DodoNA and LONG-PD Study
Bibliographic record
Abstract
Background: Different factors influence severity, progression and outcomes in Parkinson’s disease (PD). Lack of standardized clinical assessment limits comparison of outcomes and availability of well-characterized cohorts for collaborative studies. Methods: We developed structured clinical documentation support (SCDS) and launched the DNA Predictions to Improve Neurological Health (DodoNA) project eight years ago to standardize clinical assessment and identify molecular predictors of disease progression. The Longitudinal Clinical and Genetic Study of Parkinson’s Disease (LONG-PD) cohort was launched five years ago using a Research Electronic Data Capture (REDCap) format mirroring the DodoNA SCDS within the Genetic Epidemiology of Parkinson’s disease (GEoPD) consortium. Four sites in different countries participated in the LONG-PD study. Demographics, education, exposures, age at onset (AAO), Unified Parkinson’s Disease Rating Scale (UPDRS) parts I-VI or Movement Disorders Society (MDS)-UPDRS, Montreal Cognitive Assessment (MOCA)/Short Test of Mental Status (STMS)/ Mini Mental State Examination (MMSE), Geriatric Depression Scale (GDS), Epworth Sleepiness Scale (ESS), dopaminergic therapy, family history, nursing home placement, death and blood samples were collected. 658 participants from the DodoNA cohort with six years of follow-up and 496 participants from the LONG-PD cohort with up to three years of follow-up were included. Group-based trajectory modeling (GBTM) analysis focused on: AAO, education, family history, MMSE/MoCA/STMS., UPDRS II-II, UPDRS-III tremor and bradykinesia sub-scores, H&Y stage, disease subtype and dopaminergic therapy. The DodoNA cohort served as the training and the LONG-PD cohort as the test set. Results: Both cohorts show separation of patients in a slowly progressing and a rapidly progressing course. AAO, MMSE score, H &Y stage, UPDRS-III tremor and bradykinesia sub-scores classified patients in either group. Late AAO and male sex, assigned patients to the rapidly progressing group, whereas tremor to the slower progressing group. Patient classification occurs relatively early in the disease course. Conclusions: Standardized clinical assessment provides accurate characterization of the clinical phenotype in pragmatic clinical settings. Trajectory analysis demonstrated two different trajectories of disease progression, identified determinants of classification and demonstrated SCDS’s utility in developing well characterized cohorts which in conjunction with genomic analysis can elucidate disease etiology, leading to targeted therapies that can improve disease outcomes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 0.000 |
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.
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".