Prevalence of mild behavioral impairment in mild cognitive impairment and subjective cognitive decline, and its association with caregiver burden
Bibliographic record
Abstract
BACKGROUND: Mild behavioral impairment (MBI) describes later life acquired, sustained neuropsychiatric symptoms (NPS) in cognitively normal individuals or those with mild cognitive impairment (MCI), as an at-risk state for incident cognitive decline and dementia. We developed an operational definition of MBI and tested whether the presence of MBI was related to caregiver burden in patients with subjective cognitive decline (SCD) or MCI assessed at a memory clinic. METHODS: MBI was assessed in 282 consecutive memory clinic patients with SCD (n = 119) or MCI (n = 163) in accordance with the International Society to Advance Alzheimer's Research and Treatment - Alzheimer's Association (ISTAART-AA) research diagnostic criteria. We operationalized a definition of MBI using the Neuropsychiatric Inventory Questionnaire (NPI-Q). Caregiver burden was assessed using the Zarit caregiver burden scale. Generalized linear regression was used to model the effect of MBI domains on caregiver burden. RESULTS: While MBI was more prevalent in MCI (85.3%) than in SCD (76.5%), this difference was not statistically significant (p = 0.06). Prevalence estimates across MBI domains were affective dysregulation (77.8%); impulse control (64.4%); decreased motivation (51.7%); social inappropriateness (27.8%); and abnormal perception or thought content (8.7%). Affective dysregulation (p = 0.03) and decreased motivation (p=0.01) were more prevalent in MCI than SCD patients. Caregiver burden was 3.35 times higher when MBI was present after controlling for age, education, sex, and MCI (p < 0.0001). CONCLUSIONS: MBI was common in memory clinic patients without dementia and was associated with greater caregiver burden. These data show that MBI is a common and clinically relevant syndrome.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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".