Neuropsychology test and P300 detection characteristics analysis in early-onset Parkinson’s disease
Bibliographic record
Abstract
Objective To investigate the clinical and cognitive characteristics of early-onset Parkinson’s disease (EOPD). Methods Two hundred and forty-two Parkinson’s disease (PD) patients were divided into EOPD (≤50 year-old，n=76) and late-onset Parkinson’s disease (LOPD;>50 year-old，n=166) according to their age.The age，disease duration，family history and other general clinical features were compared between EOPD and LOPD groups.Global and various aspects of cognitive dysfunction were analysed between the 2 groups.Additionally，P300 long latency auditory evoked potentials was employed to confirm the cognition function. Results EOPD patients had a positive family history，compared with LOPD group (21.1% (16/76) vs 6.02% (10/166)，χ2=7.87，P=0.005).Montreal Cognitive Assessment (MoCA) Scale in space and executive functions，attention，delayed recall，orientation and total scores in EOPD group were significantly higher (23.89±3.31 vs 22.17±4.66;3.57±1.33 vs 3.12±1.4; 38.00±0.98 vs 4.93±1.21;2.46±1.49 vs 1.73±1.57; 5.75±0.58 vs 5.36±0.95，t=3.44，3.79，3.12，1.98，2.52，all P<0.05).Besides，LOPD patients displayed defective performance on similarity，graphic arrangement，block test and digital span (10.54±2.48 vs 8.26±2.82; 7.91±3.33 vs 6.73±2.38; 8.74±3.10 vs 7.52±2.67; 10.15±2.48 vs 9.14±2.29，t=3.30，2.62，2.58，2.53，all P<0.05).In addition，P300 latencies were markedly delayed and the P300 amplitudes were notably declined in LOPD group. Conclusions Positive family history may play an important role in the etiology of EOPD.The cognitive impairments in EOPD patients are milder than LOPD patients.Executive efficiency，space function and attention are well-preserved in EOPD. Key words: Parkinson disease; Cognition; Neuropsychological tests; Event-related potentials，P300
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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 | 0.000 |
| 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".