Evaluating Mild Cognitive Dysfunction in Patients with Parkinson’s Disease in Clinical Practice in Taiwan
Bibliographic record
Abstract
Our study aimed to examine the contribution of commonly used tools, including the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), and develop a formula for conversion of these tests in the Chinese population. We also create a predictive model for the detection of Chinese patients' mild cognitive impairment (MCI). We recruited 168 patients with Parkinson's disease (PD) from 12 medical centres or teaching hospitals in Taiwan, and each participant received a comprehensive neuropsychological assessment. Logistic regression analysis was conducted to find predictors of MCI with the help of a generalized additive model. We found that patients with an MMSE > 25 or a MoCA > 21 were less likely to have MCI. The discrimination powers of the two tests used for detecting MCI were 0.902 and 0.868, respectively, as measured by the area under the receiver operating characteristic curve (ROC). The best predictive model suggested that patients with a higher MMSE score, delayed recall scores of the 12-item Word Recall Test ≥ 5.817, and no test decline in the visuospatial index were less likely to have MCI (ROC = 0.982). Our findings have clinical utility in MCI detection in Chinese PD and need a larger sample to confirm.
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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 | 0.005 |
| 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 | 0.001 |
| 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 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".