Sex prevalence in mild behavioral impairment: a systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Mild behavioral impairment (MBI) leverages the risk associated with later‐life emergent and persistent neuropsychiatric symptoms (NPS) to identify a high‐risk group for incident cognitive decline and dementia. While sex differences in NPS prevalence have been reported in Alzheimer’s dementia, these differences have not been well explored in dementia‐free samples. Here, we investigated sex differences in MBI prevalence across normal cognition (NC) and mild cognitive impairment (MCI). We also included rater in the analyses (i.e., self, informant, clinician), given that dementia‐free older adults may self‐report symptoms or attend clinic without an informant. Methods EMBASE, MEDLINE, PsycINFO, and grey literature were systematically searched for articles mentioning MBI. Retrieved abstracts and full‐text articles were screened by two independent reviewers and included if sex‐specific MBI prevalence was reported. A standardized data extraction sheet was used for demographics, cognitive diagnosis, rater, and MBI prevalence within each sex. A risk of bias assessment was conducted. A random‐effects meta‐analysis calculated the pooled prevalence of MBI in each sex, stratified by cognitive diagnosis (NC, MCI, and combined NC/MCI samples) and symptom rater. Result A total of 5783 articles (EMBASE n = 2752, MEDLINE n = 1798, PsycINFO n = 1233) were retrieved from the search; 165 full‐texts were screened, and 64 were included for data extraction (Figure 1). In females, MBI prevalence increased as cognitive status declined from NC (25%) to mixed cognition (32%) to MCI (43%) [Table 1]. MBI prevalence was highest when reported by informants (35%), followed by clinicians (23%), and self‐reports (20%) [Table 2]. Similarly, in males, MBI prevalence increased with declining cognition (NC = 27%, NC/MCI = 37%, MCI = 48%). MBI prevalence was highest when reported by informants (40%), followed by self‐reports (23%) and clinicians (22%). Conclusion In both males and females, approximately a quarter of NC and nearly half of MCI participants had symptoms of MBI; prevalence was slightly greater in males. Informants reported MBI symptoms more than clinicians or participants themselves. These findings suggest that MBI is prevalent in dementia‐free older adults of both sexes and that informants are essential given the increasing MBI prevalence with declining cognition.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".