Transitioning from Subtyping to Precision Medicine in Parkinson's Disease: A Purpose‐Driven Approach
Bibliographic record
Abstract
The International Parkinson and Movement Disorder Society (MDS) created a task force (TF) to provide a critical overview of the Parkinson's disease (PD) subtyping field and develop a guidance on future research in PD subtypes. Based on a literature review, we previously concluded that PD subtyping requires an ultimate alignment with principles of precision medicine, and consequently novel approaches were needed to describe heterogeneity at the individual patient level. In this manuscript, we present a novel purpose-driven framework for subtype research as a guidance to clinicians and researchers when proposing to develop, evaluate, or use PD subtypes. Using a formal consensus methodology, we determined that the key purposes of PD subtyping are: (1) to predict disease progression, for both the development of therapies (use in clinical trials) and prognosis counseling, (2) to predict response to treatments, and (3) to identify therapeutic targets for disease modification. For each purpose, we describe the desired product and the research required for its development. Given the current state of knowledge and data resources, we see purpose-driven subtyping as a pragmatic and necessary step on the way to precision medicine. © 2024 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 | 0.320 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.008 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.005 | 0.011 |
| 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".