Cognitive profile of mild behavioral impairment in Brain Health Registry participants
Bibliographic record
Abstract
Abstract INTRODUCTION Dementia assessment includes cognitive and behavioral testing with informant validation. Conventional testing is resource intensive, with uneven access. Online unsupervised assessments could reduce barriers to risk assessment. We interrogated the relationship between informant-rated behavioral changes and neuropsychological test performance in older adults in the Brain Health Registry. METHODS Participants completed online unsupervised cognitive tests, and informants completed the Mild Behavioral Impairment Checklist via a Study Partner portal. Cognitive performance was evaluated in MBI+/- individuals, as was the association between cognitive scores and MBI symptom severity. RESULTS Mean age of the 499 participants was 67, 61% of which were female. MBI+ participants had lower working memory and executive function test scores. Lower cognitive test scores associated with greater MBI burden. DISCUSSION Our findings support the feasibility of remote, informant-reported behavioral assessment and support its validity by demonstrating a relationship to cognitive test performance using online unsupervised assessments for dementia risk assessment. RESEARCH IN CONTEXT Systematic review The authors searched MEDLINE and Google Scholar for studies linking Mild Behavioral Impairment (MBI) and cognition in non-demented older adults. Most studies have utilized transformed Neuropsychiatric Inventory scores to assess MBI, and relatively few using the novel MBI-checklist (MBI-C), with the largest study using self-report. Exploration of informant reports of MBI is important due to impaired insight that may accompany neuropsychiatric symptoms. Interpretation Older adults with online, informant reported MBI had poorer performance in memory and executive function measured using online neuropsychological testing compared to those without MBI. These findings are consistent with the current literature and suggest that the MBI-C may serve as a marker for poorer cognitive performance. Future directions Our data support the role of online testing of cognition and behavior for risk assessment. This approach to evaluate behavior and cognition can be explored further, to determine if it is a scalable, online approach to detection of neurodegenerative disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| 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".