Prevalence of mild behavioral impairment subdomains: A meta‐analysis
Bibliographic record
Abstract
Abstract Background Mild behavioral impairment (MBI) is a neurobehavioral syndrome characterized by later life emergence of neuropsychiatric symptoms. Our previous meta‐analysis showed that the pooled prevalence of MBI among cognitively normal (CN), subjective cognitive impairment (SCI) and mild cognitive impairment (MCI) subjects are 17%, 35.8% and 45.5% respectively. The main aim of this study is to calculate the pooled prevalence of MBI subdomains. Method A search of the literature on MBI in MCI, CN, and SCI and CN but at risk (CN‐AR) subjects published between 1 January 2003 and 14 January 2021 was conducted. Meta‐analysis using a random effects model was performed to determine the pooled estimate of the prevalence of MBI subdomains. Meta‐regression was performed to identify factors contributing to the variance of prevalence rate. Result Nine studies conducted among 11568 subjects (9259 CN, 1057 SCI & 1252 MCI) with retrievable subdomain data underwent meta‐analysis, revealing pooled prevalence of affective dysregulation (AFD), impulse dyscontrol (IDS), decreased motivation (DMT), social inappropriateness (SIP) and abnormal perception/ thought (APT) of 31.2% (95% CI 24.6‐38.5%), 27.9% (95% CI 22.2‐34.6%), 11.5% (95% CI 7‐g18.5%), 7.4% (95% CI 4.1‐12.9%), and 3.4% (95% CI 1.7‐6.7%) respectively. Although all subdomains demonstrate ordinal increase in pooled prevalence from CN, SCI and MCI subgroups, meta‐regression demonstrated no significant difference in subdomains prevalence among cognitive subgroups. The pooled prevalence of AFD and IDS are greater than that of SIP and APT among all cognitive subgroups. Among MCI subjects, racial group (Asian vs. Caucasians) could account for variance between the studies in prevalence of IDS (coefficient 1.5908, 95% CI 0.7850‐2.3966, R2 analogue 0.63, p<0.001). Conclusion AFD and IDS are two most prevalent MBI subdomains and remain the same with cognitive deterioration. This finding is potentially relevant to clinical practice.
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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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.054 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".