Non-Motor Symptoms in Early Drug-Naive Parkinson disease (P3.007)
Bibliographic record
Abstract
OBJECTIVE: To examine potential sex differences in non-motor symptoms (NMS) among drug-naïve Parkinson disease (PD) patients, and to identify NMS that can best differentiate early PD cases from controls. DESIGN/METHODS: Cross-sectional analysis of 414 newly diagnosed, untreated PD patients (269 male and 145 female) and 188 healthy controls (121 male and 67 female) in the Parkinson’s Progression Markers Initiative (PPMI) study. NMS were measured using well-validated instruments covering sleep, olfactory, neurobehavioral, autonomic, and neuropsychological domains. RESULTS: Male and female PD patients were fairly comparable on motor presentations, however sex differences were observed for several non-motor features. Male PD patients had significantly more pronounced deficits in olfaction (p=0.02) and multiple cognitive measurements (all p<0.01) than female patients, whereas female cases experienced higher trait anxiety (p=0.02). Multiple stepwise logistic regression analysis showed that the combination of NMS measures: University of Pennsylvania Smell Identification Test (UPSIT), Montreal Cognitive Assessment (MoCA), Scales for Outcomes in Parkinson's disease - Autonomic (SCOPA-AUT), and state anxiety from the State-Trait Anxiety Inventory effectively differentiate PD patients from controls with an area under the receiver operating characteristic curve (AUC) of 0.913 (95[percnt] confidence interval [CI]: 0.89-0.94). UPSIT, MoCA, and SCOPA-AUT were the most predictive NMS in men (AUC=0.919, 95[percnt]CI: 0.89-0.95) as compared to UPSIT, MoCA, and REM Sleep Disorder Screening Questionnaire in women (AUC=0.903, 95[percnt]CI: 0.86-0.95). CONCLUSIONS: Our analysis revealed notable sex differences in several non-motor features of de novo PD patients. Further, we found a parsimonious NMS combination that could effectively differentiate de novo cases from healthy controls.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".