Prevalence, Clinical Correlates, Cognitive Trajectories, and Dementia Risk Associated With Mild Behavioral Impairment in Asians
Bibliographic record
Abstract
Objective: Mild behavioral impairment (MBI) is characterized as later-life–emergent and persistent neuropsychiatric symptoms (NPS). The symptom persistence criterion of MBI has shown to increase the signal-to-noise ratio of the syndrome, decreasing the likelihood of false-positive NPS. However, the long-term cognitive and prognostic impact of MBI remains to be evaluated against the traditional framework of NPS, especially in Asian cohorts. This study investigated the epidemiologic characteristics of MBI in a prospective clinical cohort of Singaporean elderly. Methods: A total of 304 dementia-free individuals (mean age = 72.2 years, 51.6% female) were recruited between August 2010 and October 2019. All participants underwent annual neuropsychological, neuropsychiatric, and clinical assessments for 4 consecutive years and were diagnosed as having no cognitive impairment (NCI) or cognitive impairment–no dementia (CIND). MBI was ascertained using both baseline and year-1 Neuropsychiatric Inventory assessments. Cognitive Z-scores and Clinical Dementia Rating Sum-of-Boxes (CDR-SoB) scores were calculated. Results: The prevalence of MBI was 14.5% (7.1% of NCI, 12.9% of CIND-mild, and 24.7% of CIND-moderate patients). MBI patients showed poorer cognitive function at baseline (F1,295 = 8.13 , P = .005), primarily in memory and executive function domains. MBI was associated with accelerated decline in global cognition (β = –0.15; 95% CI, −0.23 to −0.07) along with faster increase in CDR-SoB (β = 0.92; 95% CI, 0.62 to 1.21) as compared to individuals without symptoms or transient NPS. A total of 38.6% of MBI patients developed dementia as compared to 12.3% of non-MBI elderly (χ2 = 19.29, P < .001). MBI increased risk of incident dementia by 2.56-fold as compared to no symptoms or transient NPS, regardless of cognitive impairment. Conclusions: MBI is a neurobehavioral risk factor for dementia, representing a potential target for dementia risk modeling, preventive intervention, and disease management.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".