The Non-Motor Symptom Profile of Progressive Supranuclear Palsy
Bibliographic record
Abstract
OBJECTIVE: Non-motor symptoms (NMSs) significantly contribute to increased morbidity and poor quality of life in patients with parkinsonian disorders. This study aims to explore the profile of NMSs in patients with progressive supranuclear palsy (PSP) using the validated Non-Motor Symptom Scale (NMSS). METHODS: Seventy-six patients with PSP were evaluated in this study. Motor symptoms and NMSs were evaluated using the PSP Rating Scale (PSPRS), Unified Parkinson's Disease Rating Scale-III, Montreal Cognitive Assessment, Hamilton Depression (HAM-D) and Anxiety Rating Scales, Parkinson's Disease Sleep Scale (PDSS) and NMSS. NMS severity and prevalence were also compared between patients with PSP-Richardson syndrome (PSP-RS) and those with PSP-parkinsonism. RESULTS: All subjects in this cohort reported at least 2 NMSs. The most prevalent NMSs in patients with PSP were in the domains of sleep/fatigue, mood/cognition, and sexual function. The least prevalent NMSs were in the domains of cardiovascular including falls, and perceptual problems/hallucinations. Significant correlations were observed between the NMSS scores and HAM-D, PDSS, PSPRS scores and PSPRS sub-scores. The severity of NMSs was unrelated to the duration of illness. Patients with PSP-RS reported a higher severity of drooling, altered smell/taste, depression and altered interest in sex and a higher prevalence of sexual dysfunction. CONCLUSION: NMSs are commonly observed in patients with PSP, and the domains of sleep, mood and sexual function are most commonly affected. These symptoms contribute significantly to disease morbidity, and clinicians should pay adequate attention to identifying and addressing these symptoms.
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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 | 0.000 | 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".