PDQ-8: A Simplified and Effective Tool Measuring Life Quality in Progressive Supranuclear Palsy
Bibliographic record
Abstract
BACKGROUND: The self-reported quality of life (QoL) should be carefully listened to in progressive supranuclear palsy (PSP) from the patient-centered perspective. However, there was still a lack of short QoL measurement tool in atypical parkinsonism. OBJECTIVE: We aimed to test whether the short Parkinson's Disease Questionnaire-8 (PDQ-8) was effective in assessing QoL in PSP, comparing with Progressive Supranuclear Palsy Quality of Life Scale (PSP-QoL) and Parkinson's Disease Questionnaire-39 (PDQ-39). METHODS: 132 patients with clinical diagnosed PSP, including PSP-Richardson syndrome (RS) subtype (n = 71) and PSP-non-RS subtype (n = 61) were recruited for clinical evaluation including QoL assessment. The detailed QoL profiles and possibility of using PDQ-8 were systemically analyzed. The determinants to the QoL were then calculated by multivariate linear regression analysis. RESULTS: The PSP-QoL total score summary index (SI) was 22.8 (10.1, 41.1), while the PDQ-8 and PDQ-39 total SI score were 28.1 (12.5, 46.9) and 29.5 (15.4, 49.4). Mobility, activities of daily life, cognition and communication were the main affected QoL subdomains (median SI: 40.0, 31.3, 25.0 and 25.0 respectively). PSP-RS subtype showed more severe damage physically (p<0.001) and mentally (p = 0.002) compared to other subtypes. More importantly, the strong relevance of PDQ-8 and recommended PSP QoL tools were confirmed (p<0.001). In addition, disease severity, depression and daytime sleepiness were proved to be critical determinants for QoL in PSP. CONCLUSIONS: PDQ-8 could be an easy, reliable, and valid tool to evaluate QoL in patients with PSP. Besides motor symptoms, more attention should be paid to non-motor impairment such as depression in PSP.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".