Prevalence estimates of mild behavioral impairment in a population-based sample of pre-dementia states and cognitively healthy older adults
Bibliographic record
Abstract
BACKGROUND: A dearth of population-based epidemiological research examines neuropsychiatric symptom (NPS) in sub-clinical populations across the spectrum from normal aging to mild cognitive impairment (MCI). The construct of mild behavioral impairment (MBI) describes the emergence of sustained and impactful NPS in advance of or in combination with MCI. This is the first epidemiological study to operationalize the recently published diagnostic criteria for MBI and determine prevalence estimates across the spectrum from cognitively normal to MCI. METHODS: MBI was assessed in 1,377 older (age range 72-79 years; 52% male; MCI ;= 133; cognitively normal, but-at-risk = 397; cognitively healthy = 847). MBI was assessed in accordance with the ISTAART-AA diagnostic criteria for MBI using the neuropsychiatric inventory. RESULTS: 34.1% of participants met the criteria for MBI. High prevalence of MBI across the cognitive spectrum was reported (48.9% vs. 43.1% vs. 27.6%). Irrespective of level of cognitive impairment, impulse dyscontrol (33.8% vs. 28.7% vs. 17.2%) and decreased motivation (32.3% vs. 26.2% vs. 16.3%) were the most frequently met MBI domains. MBI was more prevalent in men (χ2 = 4.98, p = 0.026), especially the domains of decreased motivation and impulse dyscontrol. CONCLUSIONS: This study presents the first population-based prevalence estimates for MBI using the recently published ISTAART-AA diagnostic criteria. Findings indicate relatively high prevalence of MBI in pre-dementia clinical states and amongst cognitively healthy older adults. Findings were gender-specific, with MBI affecting more men than women. Knowing the estimates of these symptoms in the population is essential for understanding and differentiating the very early development of clinical disorders.
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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 | 0.002 | 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".