Characterising mild behavioural impairment in Asian mild cognitive impairment and cognitively normal individuals
Bibliographic record
Abstract
Abstract Background Mild Behavioural Impairment (MBI) is a neurobehavioral syndrome characterized by later‐life emergent and sustained neuropsychiatric symptoms that is associated with higher risk of incident cognitive decline and dementia. While MBI is common in both subjective and mild cognitive impairment (MCI), most findings are based on Caucasians and the literature of MBI among Asians remains sparse. Here, we aim to investigate the frequency of MBI and its relationship with cognition, sleep and mood symptoms among cognitively intact (CN) and MCI Asians. Method 162 subjects (80 CN and 82 MCI) with a Clinical Dementia Rating of 0 or 0.5 were recruited from an outpatient neurology clinic (National Neuroscience Institute, Singapore). All subjects were administered a comprehensive neuropsychological assessment, the Pittsburgh Sleep Quality Index (PSQI) and the MBI‐checklist (MBI‐C). The presence of MBI was determined based on published cut‐offs of 6.5 for MCI and 8.5 for CN. Regression models evaluated the relationships between MBI and cognition, sleep and mood. Result 29 out of 162 subjects (17.9%) had MBI (28.0% of MCI and 7.5% of CN). Subjects with MBI had poorer global cognition (p=.029) and attention/working memory (p=.049). Specifically, we found that interest and impulse control subdomains of the MBI‐C were associated with poorer performance on the global cognition tests. In addition, those with MBI had sleep‐related daytime dysfunction (p=.008), poorer GDS and DASS scores (p<.01 or p<.001). Conclusion MBI in common among our Asian cohort and is associated with poorer cognitive performance, sleep and mood symptoms. Our findings further support the utility of MBI in clinical practice to identify individuals with early presentation of neurodegenerative diseases so as to provide a window of opportunity for early interventions to improve clinical outcomes.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".