Clinical investigation of mild cognitive impairment in patients with Parkinson's disease
Bibliographic record
Abstract
Objective To describe the prevalence and neuropsychological character of mild cognitive impairment (MCI) associated with Parkinson' s disease(PD-MCI). Methods One hundred and three PD patients and a control group of 32 healthy old subjects were chosen. Psychometric assessment included the Mini Mental State Examination, the Dementia Rating Scale and a series of neuropsychol ogicaltests. The Hamilton Rating Scale of Depression was used to assess depression in PD patients. Results (1)Twenty-one (20.4%) PD patients was diagnosed with dementia, 45 (43.7%) had a MCI and only 37(35.9%) had no cognitive impairment; (2) Subjects with PD-MCI were older, had a later onset of the PD,and displayed more severe motor symptoms compared with those without cognitive impairment; (3) The prevalence and neuropsychological profile of PD-MCI were thought to correlate with the dominating side and subtype of Parkinsonian symptoms, for patients with left-sided dominant symptoms had a significantly higher chance of suffering MCI than those with right-sided dominant symptoms, the ratio being 74.2% vs 42.2%,χ<'2 =7. 589,P <0.05; The tremor-dominant group took less time than the mixed group for Stroop word test measurement ((80.8±39.9) s vs (94.4±30.0) s,t=3.332,P<0.01). Conclusion Identification of MCI is of important clinical significance, which helps to treat patients differently and thus predict the prognosis. Key words: Pakinson disease; Congnition disorders; Prevalence; Neuropsychological tests
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".