The Impact of Neuropsychiatric Symptoms in Perceived Quality of Life in Patients With Progressive Supranuclear Palsy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Progressive supranuclear paralysis (PSP) is an atypical parkinsonian disorder associated with oculomotor features, motor disturbances, along with cognitive problems and neuropsychiatric symptoms. Quality of life (QoL) is often assessed in relation to the motor symptoms. Our aim was to investigate the impact of neuropsychiatric symptoms on QoL. METHODS: We used data from 40 patients meeting criteria for probable PSP from the Rossy PSP Centre. Motor and neuropsychiatric symptoms, cognition, functionality, disease severity, and quality of life were examined using validated scales. We performed a linear regression model using multiple imputations with chained equations with 500 sets. RESULTS: We found that lower quality of life scores were related to higher anxiety and depression and the interaction between these two. There was a decrease in the quality of life of -3.7 points (95% CI: -6.1 to -1.1) for every one point of increase on the depression scale; there was a decrease in the quality of life scale of -4.3 points for each point of increase on the depression score (95% CI: -7.8 to -0.8). The fully adjusted linear model showed that motor scores, cognition, and other neuropsychiatric symptoms were not associated with quality of life. CONCLUSION: We found that both anxiety and depression significantly impacted quality of life. Given the prevalence of non-motor manifestations in PSP, these results emphasize the importance of comprehensive evaluations to better capture the multi-faceted impairments seen in PSP that have a meaningful impact on the patient's life.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".