Assessing Mild Behavioral Impairment with the Mild Behavioral Impairment-Checklist in People with Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Neuropsychiatric symptoms (NPS) are non-cognitive, behavioral, or psychiatric symptoms, common in mild cognitive impairment (MCI) and associated with a higher risk of dementia. Mild behavioral impairment (MBI) is a validated diagnostic entity, that describes the emergence of later life NPS in pre-dementia states. The Mild Behavioral Impairment Checklist (MBI-C) is the first measure developed to assess MBI. OBJECTIVE: To estimate the prevalence of MBI in people with MCI and to study the score distribution, sensitivity, specificity, diagnostic utility of the MBI-C, and its correlations with neuropsychological tests. METHODS: One hundred eleven MCI participants were evaluated with the Questionnaire for Subjective Memory Complaints (QSMC), Mini-Mental State Examination, Cambridge Cognitive Assessment-Revised, Neuropsychiatric Inventory-Questionnaire (NPI-Q), Geriatric Depression Scale-15 items (GDS-15), Lawton and Brody Index, and the MBI-C, which was administered by phone to participants' informants. Descriptive, logistic regression, ROC curve, and bivariate correlations analyses were performed. RESULTS: MBI diagnosis prevalence was 14.2%. The total MBI-C score differentiated people with MBI at a cutoff-point of 6.5, optimizing sensitivity and specificity. MBI-C total score correlated positively with NPI-Q, QSMC, GDS-15, and Lawton and Brody Index. CONCLUSION: The total MBI-C score, obtained by phone administration, is sensitive for detecting MBI in people with MCI. The MBI-C scores indicated that MCI participants had subtle NPS that were correlated to their subjective memory complaints reported by informants, depressive symptoms, and negatively with Instrumental Activities of Daily Living. Further research should be done to clarify the predictive role of NPS in MCI for incident dementia.
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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 | 0.002 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".