The challenges with the identification of Parkinson’s disease subtypes
Bibliographic record
Abstract
INTRODUCTION: Parkinson's disease (PD) exhibits distinct phenotypes with specific pathophysiological features. Their definition is essential to inform therapeutic choices and trials design. AREAS COVERED: We searched in September 2025 PubMed/MEDLINE, Scopus, and Web of Science to review current PD stratifications strategies based on clinical, tissue, and imaging biomarkers, highlighting their specific strengths and limitations. We provide an overview of the proposed pathophysiological mechanisms underlying distinct phenotypes and the open challenges in the field. EXPERT OPINION: Subtyping of PD based on clinical phenotype is rapidly evolving, driven by advances in understanding its pathological mechanisms and clinical heterogeneity. Identifying distinct PD phenotypes is essential to deliver personalized care and optimize clinical trial design, particularly for disease-modifying therapies. Neurotransmitter-based subtyping (cholinergic/noradrenergic/serotonergic) provides a biologically grounded framework that partially overlaps with anatomical models such as the brain-first/body-first distinction. However, validity of such models is also controversial, especially in more advanced stages of PD where many pathways merge. Integrating multimodal data, including clinical/imaging/biomarker/genetic measures, is crucial to improve stratification accuracy and account for comorbidities/copathology. Future progress relies on hypothesis-supervised data-driven approaches, longitudinal validation, and globally inclusive cohorts to achieve robust, biologically informed, and clinically meaningful PD subtyping.
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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 | 0.062 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".