Profile and progression of cognitive deficits in Progressive Supranuclear Palsy, Multiple System Atrophy and Parkinson's Disease
Bibliographic record
Abstract
Objective: This study investigated neuropsychological tests most sensitive in differentiating progressive supranuclear palsy (PSP) from Parkinson’s disease (PD) and multiple system atrophy (MSA), and in detecting cognitive changes at follow-up. \n \nBackground: Cognitive impairment is frequent in PSP, but its characteristics and progression need to be properly defined. \n \nMethod: We evaluated 35 PSP with Richardson’s syndrome (PSP-RS), 30 MSA as well as 65 age-, sex-, and education-matched PD with an extensive clinical and neuropsychological assessment, allowing Level II cognitive diagnosis. Eighteen PSP, 12 MSA and 30 PD had a second evaluation 12-18-month (mean 15 months) after the first assessment. \n \nResults: In PSP, Montreal Cognitive Assessment (MoCA), verbal fluencies (phonemic and semantic tasks), Stroop test, Digit Span Sequencing (DSS), incomplete letters of Visual Object and Space Perception (VOSP) and Benton’s Judgment of Line Orientation (JLO) performance were significantly impaired at baseline compared to PD and MSA. Executive and visuo-spatial abilities declined longitudinally in PSP, but not in PD and MSA. After 1.5 year, 16% of PSP converted to dementia. \n \nConclusion: Our study provides evidence that cognitive decline is more severe and rapid in PSP than PD and MSA. MoCA, verbal fluency, DSS and Benton’s JLO are valuable tests to detect cognitive progression in PSP and may be proposed as biomarker for research protocols to assess efficacy of disease modification strategies.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".