Characteristic Motor and Nonmotor Symptoms Related to Quality of Life in Drug-Naïve Patients with Late-Onset Parkinson Disease
Bibliographic record
Abstract
BACKGROUND/AIMS: Unlike young-onset Parkinson disease (YOPD), characteristics of late-onset PD (LOPD) have not yet been clearly elucidated. We investigated characteristic features and symptoms related to quality of life (QoL) in LOPD patients. METHODS: We recruited drug-naïve, early PD patients. The patient cohort was divided into 3 subgroups based on patient age at onset (AAO): the YOPD group (AAO <50 years), the middle-onset PD (MOPD) group, and the LOPD group (AAO ≥70 years). Using various scales for motor symptoms (MS) and non-MS (NMS) and QoL, we compared the clinical features and impact on QoL. RESULTS: Of the 132 enrolled patients, 26 were in the YOPD group, 74 in the MOPD group, and 32 in the LOPD group. Among parkinsonian symptoms, patients in the LOPD group had a lower score on the Korean version of the Montreal Cognitive Assessment than the other groups. Logistic regression analysis showed genitourinary symptoms were related to the LOPD group. Linear regression analysis showed both MS and NMS were correlated with QoL in the MOPD group, but only NMS were correlated with QoL in the LOPD group. Particularly, anxiety and fatigue affected QoL in the LOPD group. CONCLUSION: LOPD patients showed different characteristic clinical features, and different symptoms were related with QoL for LOPD than YOPD and MOPD patients.
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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 | 0.001 |
| 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) | 0.001 | 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".