The Mild Behavioral Impairment Checklist (MBI-C): A Rating Scale for Neuropsychiatric Symptoms in Pre-Dementia Populations
Bibliographic record
Abstract
BACKGROUND: Mild behavioral impairment (MBI) is a construct that describes the emergence at ≥50 years of age of sustained and impactful neuropsychiatric symptoms (NPS), as a precursor to cognitive decline and dementia. MBI describes NPS of any severity, which are not captured by traditional psychiatric nosology, persist for at least 6 months, and occur in advance of or in concert with mild cognitive impairment. While the detection and description of MBI has been operationalized in the International Society to Advance Alzheimer's Research and Treatment - Alzheimer's Association (ISTAART-AA) research diagnostic criteria, there is no instrument that accurately reflects MBI as described. OBJECTIVE: To develop an instrument based on ISTAART-AA MBI criteria. METHODS: Eighteen subject matter experts participated in development using a modified Delphi process. An iterative process ensured items reflected the five MBI domains of 1) decreased motivation; 2) emotional dysregulation; 3) impulse dyscontrol; 4) social inappropriateness; and 5) abnormal perception or thought content. Instrument language was developed a priori to pertain to non-demented functionally independent older adults. RESULTS: We present the Mild Behavioral Impairment Checklist (MBI-C), a 34-item instrument, which can easily be completed by a patient, close informant, or clinician. CONCLUSION: The MBI-C provides the first measure specifically developed to assess the MBI construct as explicitly described in the criteria. Its utility lies in MBI case detection, and monitoring the emergence of MBI symptoms and domains over time. Studies are required to determine the prognostic value of MBI for dementia development, and for predicting different dementia subtypes.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".