Validation of the Mild Behavioral Impairment-Checklist in Subjective Cognitive Decline, Mild Cognitive Impairment and Dementia
Bibliographic record
Abstract
Introduction: Neuropsychiatric symptoms (NPS) are early markers of dementia preceding cognitive impairment. The Mild Behavioral Impairment Checklist (MBI-C) was developed to characterize NPS for pre-dementia patients. This thesis examines the validity and utility of the MBI-C for detecting neuropsychiatric symptoms in relation to cognition. Objectives: This study was conducted in three parts: 1) To compare factors of the MBI-C and Neuropsychiatric Inventory-Questionnaire (NPI-Q) using factor analysis. 2) To determine the association between baseline MBI-C and NPI-Q severity scores with cognition. 3) To determine the predictive utility of MBI-C and NPI-Q severity scores for change in cognition. Methods: Patients diagnosed with subjective cognitive decline, mild cognitive impairment and dementia were sampled from a cognitive neurology clinic. 1) Exploratory factor analysis was conducted to determine MBI-C and NPI-Q domains. 2) The association between baseline MBI-C and NPI-Q severity and cognition, as measured by the Montreal Cognitive Assessment (MoCA), was modelled using linear regression analysis. 3) The association between MBI-C and NPI-Q severity at baseline and change in cognition per six months was modelled using generalized linear mixed models. Results: 1) The MBI-C is a valid five-factor questionnaire with the following domains: apathy, mood/anxiety, impulse dyscontrol, social inappropriateness, and psychosis. Anhedonia and appetite disturbances are features that load onto apathy. The NPI-Q is a one-factor questionnaire in our sample. (2) Higher MBI-C and NPI-Q severity is associated with decreased cognition. MBI prevalence increases with increasing severity of cognitive diagnosis. All MBI-C domains are significantly associated with lower MoCA. Psychosis is most strongly associated and total score is most weakly associated. The MBI-C identifies age, sex and diagnosis-specific estimates. 3) Baseline MBI-C and NPI-Q scores predict cognitive decline over time. Impulse dyscontrol, mood/anxiety and social inappropriateness are most predictive of cognitive decline. Conclusions: Neuropsychiatric symptoms are associated with cognitive decline in pre-dementia and dementia patients. The MBI-C is a valid five-factor questionnaire for detecting NPS and is especially robust in pre-dementia patients. MBI domains are indicative and predictive of cognitive decline and can be targeted for management of NPS. The NPI-Q is not as applicable to pre-dementia and does not fully capture NPS groupings. The MBI-C and NPI-Q act as complements and both should be administered with consideration of patient status.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".