Towards a Biological Definition of Parkinson’s Disease
Bibliographic record
Abstract
With the growing hope that disease-modifying treatments could target the molecular basis of neurodegenerative diseases even before the onset of symptoms, there is mounting pressure to define disease entities based on pathophysiology rather than on clinical syndromes. The Alzheimer’s disease research community has recently transitioned from diagnostic criteria based on an amnestic syndrome to a purely biomarker-based disease definition, relying on the demonstration of amyloid-beta pathology, tau pathology, and neurodegeneration. In contrast, current diagnostic criteria for Parkinson’s disease still rely on the presence of the well-described clinical syndrome of parkinsonism, with the addition of characteristic motor- and non-motor signs and symptoms. However, there is now unequivocal evidence that Parkinson’s disease starts years before the onset of parkinsonism. Furthermore, neuropathologically defined Lewy body disease is clinically heterogeneous, combining a range of motor, non-motor, dopaminergic and non-dopaminergic features. Finally, clinically defined Parkinson’s disease has diverse underlying etiologies most, but not all, associated with α-synuclein positive Lewy pathology. In light of recent scientific advances, we propose a biologically based definition for the diagnosis of Parkinson's disease, initially to be used for research purposes. The criteria use a three-component ‘G-S-N’ system. The first is documentation of defined gene variants (‘G’), which cause or strongly predispose to PD as the most upstream component. The second is α-synuclein pathology (‘S’), currently defined as pathological α-synuclein deposition in tissue or positive α-synuclein seeding assays. The third is evidence of underlying neurodegeneration (‘N’), currently defined by specific neuroimaging procedures. The associated clinical syndrome (‘C’) is defined by a single high-specificity feature or multiple lower-specificity features. Initiating this transition will enable the field to fuel both basic and clinical research and move closer to the precision medicine required to develop clinically meaningful disease-modifying therapies. We acknowledge current limitations, ethical implications, and the need for prospective validation of this approach.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
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".