Parkinson's disease mild cognitive impairment classifications and neurobehavioral symptoms
Bibliographic record
Abstract
BACKGROUND: We apply recently recommended Parkinson's disease mild cognitive impairment (PD-MCI) classification criteria from the movement disorders society (MDS) to PD patients and controls and compare diagnoses to that of short global cognitive scales at baseline and over time. We also examine baseline prevalence of neuropsychiatric symptoms across different definitions of MCI. METHODS: 51 PD patients and 50 controls were classified as cognitively normal, MCI, or demented using MDS criteria (1.5 or 2.0 SD below normative values), Clinical Dementia Rating Scale (CDR), and the Dementia Rating Scale (DRS). All subject had parallel assessment with the Neuropsychiatric inventory (NPI). RESULTS: We confirmed that PD-MCI (a) is frequent, (b) increases the risk of PDD, and (c) affects multiple cognitive domains. We highlight the predictive variability of different criteria, suggesting the need for further refinement and standardization. When a common dementia outcome was used, the Level II MDS optimal testing battery with impairment defined as two SD below norms in 2+ tests performs the best. Neuropsychiatric symptoms were more common in PD across all baseline and longitudinal cognitive classifications. CONCLUSIONS: Our results advance previous findings on the utility of MDS PD-MCI criteria for PD patients and controls at baseline and over time. Additionally, we emphasize the possible utility of other cognitive scales and neuropsychiatric symptoms.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".