Non-motor subtypes in candidates for subthalamic deep brain stimulation for Parkinson's disease
Bibliographic record
Abstract
INTRODUCTION: Patients with Parkinson's Disease (PD) vary markedly in terms of non-motor symptoms (NMS) as the disease progresses. To improve PD management and clinical-trial assessment, we aimed to determine NMS endotypes in a cohort of patients with advanced PD. METHODS: We conducted an ancillary cluster analysis of the 2013-2018 cohort (n = 722) of PREDISTIM. In this French multicenter interventional cohort, consecutive candidates for subthalamic deep brain stimulation undergo thorough assessment of motor symptoms (MS) and NMS at the inclusion visit. The NMS data are based on the MDS-UPDRS, Montreal Cognitive Assessment (MoCA), and several psychiatric scales: Hamilton Anxiety Rating Scale (HAM-A), Hamilton Depression Rating Scale (HAM-D), and Lille Apathy Rating Scale (LARS). Cluster analysis with 17 NMS was conducted to identify groups with homogenous NMS profiles. RESULTS: Three distinct NMS clusters were identified. The largest had mild MS. The smallest had moderate MS and the most severe NMS, including cognitive and psychiatric dysfunction. The middle-large group had moderate MS and NMS but was distinguished by having the worst sleeping problems. The clusters did not differ in onset age or patient age and may be underpinned by disparate patterns of anatomical brain damage. CONCLUSION: The mainly-motor (Cluster 1), mainly non-motor (Cluster 3), and intermediate (Cluster 2) NMS endophenotypes must be replicated in an independent cohort but may help stratify patients for management (pharmacological, deep-brain stimulation, and non-pharmacological treatments) and inclusion and assessment in clinical trials.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".