Comparison of Montreal cognitive assessment and Mattis dementia rating scale in the preoperative evaluation of subthalamic stimulation in Parkinson’s disease
Bibliographic record
Abstract
INTRODUCTION: The preoperative evaluation of Parkinson's Disease (PD) patients for subthalamic nucleus deep brain stimulation (STN-DBS) includes the assessment of the neuropsychological status of the patient. A widely used preoperative test is the Mattis Dementia rating scale (MDRS). However, the Montreal cognitive assessment (MoCA) has also been proven to be a sensitive, time-sparing tool with high diagnostic validity in PD. We evaluate the utility of the MoCA as a preoperative screening test for PD patients undergoing bilateral STN-DBS. METHODS: In this single-centre, retrospective study, we analysed pre- and postoperative assessments of MoCA, MDRS, Movement disorder society-Unified PD Rating Scale-motor examination, PD Questionnaire-39 and levodopa equivalent daily dose. Longitudinal outcome changes were analysed using paired t-test, Pearson's correlation coefficient, linear regression and CHAID (chi-square automatic interaction detector) regression tree model. RESULTS: Clinical motor and cognitive scores of 59 patients (61.05±7.73 years, 24 females) were analysed. The MoCA, but not the MDRS, identified significant postoperative cognitive decline in PD patients undergoing STN-DBS. The preoperative MoCA score correlated with postoperative quality of life improvement, whereas the MDRS did not. PD patients with a MoCA score ≤ 23 points had a significant decline of quality of life after DBS surgery compared to patients > 23 points. CONCLUSION: This study identifies the MoCA as an alternative test within the preoperative evaluation of PD patients for the detection of neuropsychological deficits and prediction of the postoperative improvement of quality of life.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".