[P2–274]: MILD BEHAVIORAL IMPAIRMENT: WHAT ARE THE RISK FACTORS?
Bibliographic record
Abstract
Little is known about the genesis and predictors of Mild Behavioural Impairment (MBI), a construct describing the emergence of sustained and impactful neuropsychiatric symptoms in advance of or in combination with Mild Cognitive Impairment (MCI) (Ismail et al., 2016). This is the first epidemiological study to examine the role of biopsychosocial factors as risk factors for MBI 12 years later. 1377 older adults (age range 72–79 years; 52% male) with normal and preclinical cognition at wave 4 of the PATH Through Life Project (MCI=133; ‘cognitively normal, but-at-risk’ = 397; cognitively healthy = 847). Baseline depressive symptoms (PHQ-9), self-reported cardio-metabolic conditions (e.g., diabetes, heart disease), number of medications, neurological events (e.g., stroke, TIA, head injury, infection), physical activity, social engagement, negative social support and behavioural activation (BISBAS) were examined as risk factors for MBI 12 years later. MBI was assessed in accordance with the ISTAART-AA diagnostic criteria for MBI using the Neuropsychiatric Inventory. Univariate associations were found between cardio-metabolic conditions (OR=3.98, 95% CI: 1.80–8.78), number of medications (OR=3.44, 95% CI: 1.84–6.43), negative social support (OR=1.18, 95% CI: 1.10–1.26), depression (OR=1.15, 95% CI: 1.11–1.19), female gender (OR=0.78, 95% CI: 0.62–0.97), social engagement (OR=0.47, 95% CI: 0.27–0.83) and a higher risk of MBI 12 years later. Multivariate analyses showed an increased risk of MBI for number of medications (OR=2.22, 95% CI: 1.11–4.44), negative social support (OR=1.12, 95% CI: 1.04–1.25), depression (OR=1.12, 95% CI: 1.07–1.17) and female gender (OR=0.74, 95% CI: 0.58–0.94) when all variables were adjusted for. Gender interactions were not significant. In a subclinical population-based sample, biopsychosocial factors of depression, negative social support, number of medications and female gender were associated with a higher risk of MBI 12 years later. Our findings are the first to investigate modifiable risk factors for MBI and highlight the need to consider the interplay between biological, psychological and social factors in the context of MBI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".