Defining the Riddle in Order to Solve It: There Is More Than One “Parkinson's Disease”
Bibliographic record
Abstract
BACKGROUND: More than 200 years after James Parkinsondescribed a clinical syndrome based on his astute observations, Parkinson's disease (PD) has evolved into a complex entity, akin to the heterogeneity of other complex human syndromes of the central nervous system such as dementia, motor neuron disease, multiple sclerosis, and epilepsy. Clinicians, pathologists, and basic science researchers evolved arrange of concepts andcriteria for the clinical, genetic, mechanistic, and neuropathological characterization of what, in their best judgment, constitutes PD. However, these specialists have generated and used criteria that are not necessarily aligned between their different operational definitions, which may hinder progress in solving the riddle of the distinct forms of PD and ultimately how to treat them. OBJECTIVE: This task force has identified current in consistencies between the definitions of PD and its diverse variants in different domains: clinical criteria, neuropathological classification, genetic subtyping, biomarker signatures, and mechanisms of disease. This initial effort for "defining the riddle" will lay the foundation for future attempts to better define the range of PD and its variants, as has been done and implemented for other heterogeneous neurological syndromes, such as stroke and peripheral neuropathy. We strongly advocate for a more systematic and evidence-based integration of our diverse disciplines by looking at well-defined variants of the syndrome of PD. CONCLUSION: Accuracy in defining endophenotypes of "typical PD" across these different but interrelated disciplines will enable better definition of variants and their stratification in therapeutic trials, a prerequisite for breakthroughs in the era of precision medicine. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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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 | 0.064 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.009 | 0.033 |
| Scholarly communication | 0.016 | 0.038 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.010 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 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".