P2‐176: Prevalence of Mild Behavioral Impairment (MBI) in a Memory Clinic Population and the Impact on Caregiver Burden
Bibliographic record
Abstract
Neuropsychiatric symptoms (NPS) have been associated with cognitive decline in persons with MCI or normal cognition. Recently, Mild Behavioural Impairment (MBI) has been proposed as a syndrome by an Alzheimer’s Association consensus group , which defines later-life acquired NPS as possible harbingers of neurodegeneration. These NPS are described in the domains of apathy, mood, impulse control, social appropriateness, and psychosis. However, there are few data on the prevalence of NPS and the affected domains in symptomatic patients with MCI or subjective cognitive decline. We report the prevalence and characteristics of NPS in a memory clinic population, grouped by MBI domains. We analyzed neuropsychiatric inventory questionnaires (NPI-Q) from 282 consecutive patients with subjective cognitive decline or MCI, recruited from 1 January 2010 to 30 September 2015. Zarit Caregiver Burden Scale (15 item) was used to determine caregiver burden (n=245, 87%). Descriptive statistics were performed to identify frequency of NPS (present or absent) by domain, with Chi square tests performed when appropriate. Mean age was 60.7 with 13.8 years of education. The prevalence of any NPS was 81.6% (n=230). For MBI domains frequencies of NPS were: 1) mood 77.8%; 2) impulse control 64.4%; 3) apathy 51.7%; 4) social appropriateness 27.8%; and 5) psychosis 8.7%. Any NPS were reported in 76.5% of SCD and 83.5% of MCI participants. There was no gender or age difference in NPS prevalence (p=.63; p=.56). Mean MoCA (23.5) and MMSE (27.8) scores in participants with NPS were not significantly different than participants without NPS (p=.25; p=.18). However, mean caregiver burden scores were significantly greater in MBI (19.1 vs. 5.4; p<.001).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".