Mild behavioral impairment: Prevalence, clinical profile, and prognosis in a memory clinic cohort in Singapore
Bibliographic record
Abstract
Abstract Background Mild behavioral impairment (MBI) is characterized as later‐life emergent and persistent neuropsychiatric symptoms (NPS) that are suggestive of early manifestations of Alzheimer’s Disease (AD) and dementia. This study aims to investigate the prevalence, clinical correlates, cognitive trajectories, and dementia risk of MBI in a clinical cohort. Method In a Singaporean longitudinal cohort, 309 dementia‐free participants (mean age=71.6 [SD=7.8], 52% females) underwent annual neuropsychological, neuropsychiatric, and clinical assessments for up to four years. The diagnosis of MBI was ascertained using neuropsychiatric inventory assessments at two time‐points. Global and domain‐specific cognitive Z‐scores were calculated from a comprehensive neuropsychological battery, and Clinical Dementia Rating sum‐of‐boxes (CDR‐SoB) scores were used to assess disease progression. Result A total of 72 (23.3%) participants had MBI. Cross‐sectionally, MBI patients performed poorer in global cognition (β=‐0.27 [‐0.51–0.03], p=0.026), particularly in memory (β=‐0.56 [‐0.79–0.33], p<0.001), executive function (β=‐0.33 [‐0.62–0.03], p=0.030), and visuomotor speed (β=‐0.20 [‐0.39–0.02], p=0.031) domains. On follow‐up, MBI patients showed faster decline in global cognition (β=‐0.19 [‐0.27–0.11], p<0.001) (Fig. 1) and faster increment in CDR‐SoB (β=0.96 [0.74‐1.19], p<0.001) (Fig. 2) as compared to non‐MBI elderly, regardless of cognitive status. On follow‐up, 40.3% (n=29) of MBI patients progressed to dementia, compared with 8% (n=19) of non‐MBI elderly. MBI diagnosis was associated with a significantly higher risk of dementia (Hazard Ratio=6.28 [3.51‐11.23], p<0.001) (Fig. 3). Conclusion MBI may be an early neurobehavioral marker of cognitive decline leading to dementia. Incorporating MBI evaluations into clinical assessments may identify high risk patients for dementia who may benefit from intervention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".