Comparison of the Montreal Cognitive Assessment and Mini Mental State Examination Performance in Patients with Parkinson’s disease with w Low Educational Background
Bibliographic record
Abstract
Recognition of early cognitive impairment in Parkinson´s disease (PD) is important since it represents a risk factor for developing Parkinson's disease dementia and psychosis.The Mini-Mental State Examination (MMSE) remains the most commonly used screening instrument for global cognition, even though it has not been specifically validated for use in PD subjects.More recently, the Montreal Cognitive Assessment (MoCA) test has been recommended as a better screening tool in PD.Most of these studies have been done in countries with a highly-educated population.The objective of the study is to compare the performance between the MMSE and the MoCA to screen for mild cognitive impairment in subjects with Parkinson's disease and a low education background.The MMSE and MoCA were applied to 128 subjects using a cut-off score of 26 points for cognitive impairment.Fifty-five percent were classified with cognitive impairment according to the MoCA.Forty-one percent of subjects with a normal MMSE were classified with cognitive impairment by MoCA.Results from our analysis could be directly applied to other populations with a high proportion of poorly educated subjects.
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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 | 0.002 | 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".