Assessment of cognitive impairment based on the clinical heterogeneity of idiopathic Parkinson＇s disease
Bibliographic record
Abstract
Objective To assess cognitive impairment of patients with idiopathic Parkinson＇s disease（PD）of different clinical subgroups. Methods The cluster analysis approach was used to classify PD patients on the basis of their clinical features, and then compare their cognitive function according their Montreal Cognitive Assessment （MoCA）score. Results One-hundred and six patients were divided into 5 groups by means of cluster analysis：A. young-onset（n = 35）, B. rapid disease progression（n = 6）; C. tremor dominant（n = 36）, D. haplo-non-tremor dominant（n =18）,E. non-tremor dominant with depression（n = 11）. The MoCA score and proportion of abnormal scores differed among the 5 groups significantly,especially between the subgroup E（19.00 ±5.47）and the subgroup A（23. 66 ± 3.51）（P 〈 0. 05）, the former was more seriously impaired in clock drawing task（1.73 ± 1. 01 vs.2. 66 ±0. 59 ,t = -2. 904,P =0. 013）, alertness（0. 55 ±0. 52 vs. 0. 91 ±0. 28 ,t = -2. 241 ,P =0. 045）,semantic fluency（0. 64 ±0. 51 vs. 0. 97 ±0. 17 ,t = -3. 429,P =0. 001）and orientation domains than the latter（4. 91 ± 1.38 vs. 5.80±0.47,t = -3.321,P =0.020）. Conclusions The cognitive impairment in PD patients are common as well as heterogeneous. Key words: Parkinson＇s disease; Cognitive impairment
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".