Cognitive profile of mild behavioral impairment (MBI) in brain health registry participants
Bibliographic record
Abstract
Abstract Background Non‐cognitive markers may be utilized in dementia risk assessments. Mild behavioural impairment (MBI) is a validated neurobehavioural syndrome developed by an Alzheimer’s Association‐ISTAART working group including the following criteria: 1) later life emergent and persistent neuropsychiatric symptoms; 2) representing a change from longstanding personality or patterns of behaviour; 3) not better accounted for by psychiatric conditions or life stressors. MBI as an at‐risk state for cognitive decline and dementia and may be the earliest manifestations of disease for some. We investigated whether MBI, measured with the MBI checklist, was associated with cognitive changes in older adults in the Brain Health Registry (BHR). Methods We analyzed data from 5225 participants in BHR. Participants were included if they completed Lumosity cognitive tests (memory span, reverse memory span, trailmaking, go‐no‐go) and an informant‐rated MBI‐C. We excluded participants with major neurological, psychiatric or developmental disorders. Using cutpoints to dichotomize MBI+ and MBI‐ participants (MBI‐C > 5, 6, 7), univariate ANOVA was used to determine whether people with MBI also showed impairment in cognitive test performance. ANOVAs covaried for age, sex, education, and time between test administration and MBI‐C completion. All assessments were completed remotely, using the BHR online registry. Results The final sample included 802 participants with a mean age of 66.97(SD 10.71), of which 515/802 were females (64.21%). The number and percent of MBI‐C+ participants was 62 (7.7%), 53 (6.6%), and 48 (6.0%) at cutpoints of MBI‐C >5, >6, and >7 respectively. MBI+ participants had significantly poorer memory span, poorer reverse memory span, longer trail‐making completion time, and more trail‐making errors (see Table 1 for statistical reporting). A significantly greater number of men were classified as MBI+ at all 3 cutpoints (p< 0.001). Conclusions In a large cohort of older adults in BHR, MBI assessed remotely and unsupervised using an online interface, was associated with poorer cognition in the domains of memory and executive function compared to those without MBI. Utilizing the MBI‐C in case finding may be a simple, efficient, and scalable way to detect individuals with subtle cognitive changes, for further assessment and workup, or potential inclusion in clinical trials.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".