The Mild Behavioral Impairment Checklist for Parkinson's Disease: An Ancillary Instrument in Cognitive Assessment
Bibliographic record
Abstract
BACKGROUND: Mild behavioral impairment (MBI) is a syndrome characterized by the later-life onset of neuropsychiatric symptoms (NPS) and serves as a potential marker for dementia. In Parkinson's disease (PD), MBI has been associated with worse cognition, cortical atrophy, and altered connectivity. Unlike existing instruments that assess NPS in PD, the MBI Checklist (MBI-C) leverages sustained behavioral changes to identify patients at risk of cognitive impairment and neurodegeneration. The 34-item MBI-C has yet to be validated in PD. OBJECTIVE: This study assesses the MBI-C's psychometric properties in a multicenter Canadian PD sample and proposes a revised version optimized for PD. METHODS: A total of 406 PD patients from the Canadian Open Parkinson Network were assessed with the MBI-C to investigate its construct validity. Additional evaluations, including the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS), Neuropsychiatric Inventory (NPI), and Montreal Cognitive Assessment (MoCA), were implemented to examine the concurrent and criterion validities of the checklist. RESULTS: The original MBI-C exhibited considerable floor effects (24.9%). Exploratory factor analysis revealed a 5-factor 24-item MBI-C as consistent for PD (Cronbach's α = 0.856). All intrafactor correlations were statistically significant (P < 0.05), and convergent validity was found to be higher than divergent validity. Moreover, the MBI-C demonstrated moderate concurrent validity with the NPI (intraclass correlation coefficient [ICC]: 0.633, P < 0.001). The cutoff score associated with cognitive impairment on the revised instrument was 6|7. CONCLUSIONS: Compared to the original version, the revised MBI-C exhibited enhanced psychometric properties for measuring MBI in PD. It also demonstrated acceptable specificity when related to cognitive impairment. Future psychometric research should focus on capturing the subtlest manifestations of MBI in PD, examining patient-caregiver concordance, and addressing predictive validity for cognitive decline.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".