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 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 | 0.010 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".