Prevalence of mild behavioural impairment domains: a meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Mild behavioural impairment (MBI) is a neurobehavioural syndrome characterised by later life emergence of persistent neuropsychiatric symptoms. Our previous meta-analysis showed that MBI is prevalent among cognitively normal (CN), subjective cognitive impairment (SCI) and mild cognitive impairment (MCI) subjects. This study is to calculate the pooled prevalence of MBI domains among CN, SCI, and MCI subjects. METHODS: A search of relevant literature published between 1 January 2003 and 6 August 2021 was conducted. Meta-analysis using a random effects model and meta-regression was performed. RESULTS: Ten studies conducted among 12 067 subjects (9758 CN, 1057 SCI and 1252 MCI) with retrievable MBI domains 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 32.84% (95% CI 24.44-42.5%), 26.67% (95% CI 18.24-37.23%), 12.58% (95% CI 6.93-21.75%), 6.05% (95% CI 3.44-10.42%), and 2.81% (95% CI 1.67-4.69%) respectively. AFD and APT domains demonstrated ordinal increase in pooled prevalence from CN, SCI and MCI subgroups, but meta-regression demonstrated no significant difference in MBI domains prevalence among cognitive subgroups (in contrast to the significant increase in MBI prevalence from CN to SCI to MCI). The pooled prevalence of AFD and IDS are greater than that of DMT, SIP and APT among all cognitive subgroups. Several variables were found to explain the high heterogeneity. CONCLUSIONS: AFD and IDS are the two most prevalent MBI domains 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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.054 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".