•Original article• Using the Montreal Cognitive Assessment Scale to screen for dementia in Chinese patients with Parkinson’s disease
Bibliographic record
Abstract
Background: Dementia is one of the most distressing and burdensome health problems associated with Parkinson’s disease (PD). The Montreal Cognitive Assessment scale (MoCA) is widely used to screen for dementia in PD patients, but the appropriate diagnostic cutoff score when used with Chinese PD patients is not known. Aim: Determine the appropriate diagnostic cutoff value of the Chinese version of the MoCA (MoCA-C) for Chinese PD patients and describe the characteristics of PD patients screened positive for dementia using the MoCA-C. Methods: The presence of dementia in 616 PD patients and 85 community controls was determined using the Movement Disorder Society Task Force (MDS-TF) criteria (the gold standard diagnosis). We administered the MoCA-C to these individuals and used a receiver operating characteristic (ROC) curve to identify the cutoff score of the MoCA-C that most efficiently identified dementia in both PD patients and community controls. Demographic and clinical characteristics of PD patients who were screened positive or negative for dementia using the MoCA-C were compared. Results: A MoCA-C score of 23 was the optimal cutoff score for dementia in both patients and controls. Using this cutoff score, the sensitivity and specificity of the MoCA-C in PD patients were 0.70 and 0.77, respectively;
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".