Sex differences in the prevalence of mild behavioral impairment: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Mild behavioral impairment (MBI) is an early indicator of dementia risk characterized by the later life onset of persistent changes in behaviour or personality. An under‐investigated feature of MBI is the difference in symptoms and risk based on sex. This systematic review and meta‐analysis investigates the differences in the prevalence of MBI domains based on sex. Method EMBASE, MEDLINE, PsycINFO and the grey literature were systematically searched for articles mentioning MBI. Participants with dementia were excluded. Abstracts and full‐text articles retrieved from the search were screened by two independent reviewers and included if sex‐specific prevalence data were reported. A standardized data extraction sheet was used for included articles, with data items for demographics, cognitive diagnosis, prevalence of females within each MBI domain, stratified by sex. There was insufficient evidence to look at gender. A random‐effects meta‐analysis was performed to combine the extracted data to identify pooled prevalence. Result A total of 5476 articles were retrieved from the initial search, and 3435 papers remained after deduplication. After full‐text screening, 35 papers were selected for data extraction. In normal cognition, four out of five MBI domains were more prevalent in males (decreased motivation, impulse dyscontrol, social inappropriateness and abnormal perception or thought content). In mild cognitive impairment (MCI), three out of five domains were more prevalent in males (decreased motivation, impulse dyscontrol, and abnormal perception or thought content). However, in the mixed cognitive category, four out of five domains were more prevalent in females (decreased motivation, emotional dysregulation, impulse dyscontrol and social inappropriateness (Table 1). Conclusion In this group of dementia‐free older adults, sex differences in prevalence of MBI domains varied by baseline cognitive status. Although the differences are not large, these findings raise interesting questions about sex‐specific trajectories of neuropsychiatric symptoms across the cognitive spectrum. This study highlights the need to explore differences in MBI by factors such as sex and gender.
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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".