Early Detection and Management of Cognitive Impairment in Parkinson's Disease: A Predictive Model Approach
Bibliographic record
Abstract
OBJECTIVE: The study aims to identify risk factors associated with cognitive impairment in Parkinson's disease and to develop a predictive model to facilitate early clinical detection, diagnosis, and management, thereby enhancing patient prognosis and quality of life. METHODS: A total of 351 PD patients were enrolled from the Department of Neurology at the First Hospital of Shanxi Medical University between January 2022 and December 2023. Cognitive function was evaluated using the Montreal Cognitive Assessment (MoCA) scale, and patients were subsequently categorized into cognitively normal (PD-NC) and cognitively impaired (PD-CI) groups. A logistic regression analysis was conducted to identify risk factors, and a predictive model was constructed and validated. RESULTS: Among the 351 patients with PD, 188 cases were in the PD⁃NC group and 163 cases were in the PD⁃CI group, with an incidence of cognitive impairment of 46.4%. Logistic regression analysis indicated that H-Y classification, HAMA score, homocysteine, uric acid, and folic acid were significant predictors and were incorporated into the regression equation. The constructed prediction model had an area under the receiver operating characteristic curve of 0.738. CONCLUSIONS: The cognitive function of PD patients is influenced by H-Y classification, HAMA score, homocysteine, uric acid, and folic acid. The constructed prediction model demonstrates good discrimination and calibration, providing a reference basis for early clinical identification and intervention of cognitive impairment in Parkinson's disease patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".