P.015 Long-term progression and prognosis in different subtypes of Parkinson’s disease: validation of a new multi-domain subtyping method
Bibliographic record
Abstract
Background: Parkinson’s disease (PD) varies in clinical manifestations and course of progression from person to person. Identification of distinct PD subtypes is of great priority to develop personalized care approaches. We aimed to compare long-term progression and prognosis between different PD subtypes. Methods: Data on 421 individuals with de novo early-onset PD was retrieved from Parkinson’s Progression Markers Initiative (PPMI). Using a newly developed multi-domain subtyping method (based on motor phenotype, RBD, autonomic disturbance, early cognitive deficit), we divided PD population into three subtypes at baseline: “mild motor-predominant”, “Diffuse malignant” and “Intermediate”. Rate of global progression (mixed motor and non-motor features) and developing dementia were compared between the subtypes. Results: Patients with “diffuse malignant” PD experienced 0.5 z-score further worsening of global composite outcome (p=0.017) and 2.2 further decline in MOCA score (p=0.001) after 6-years of follow-up. Hazard for MCI/dementia was significantly higher in “diffuse malignant” (HR=3.2, p<0.001) and “intermediate” (HR=1.8, p<0.001) subtypes. Individuals with “diffuse malignant” PD had the lowest level of CSF amyloid-beta (p=0.006) and SPECT striatal binding ratio (p=0.001). Conclusions: This multi-domain subtyping is a valid method to predict subgroups of PD with distinct patterns of long-term progression at drug-naïve early-stage with potential application in real-life clinical practice.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".