The Association Between Adverse Childhood Experiences and Late-Life Cognition: A Systematic Review of Cross-Sectional and Case-Control Studies
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Adverse childhood experiences (ACEs) are a recognized risk factor for unfavorable health outcomes. No prior systematic review has explored the association between ACEs and cognition in late life, a critical period for cognitive fluctuation. The objective of this review is to address the following research question: What is the association between ACEs and late-life cognition? RESEARCH DESIGN AND METHODS: Articles were obtained from PubMed, PsycINFO, and Scopus. The last search was performed in May 2021. Eligible articles examined the association between exposure to at least 1 ACE and the outcome of late-life cognition, measured either by cognitive testing or the presence/absence of a neurocognitive disorder. Data were synthesized narratively using the synthesis without meta-analysis guidelines, and the risk of bias was assessed using the Newcastle-Ottawa Scale (NOS) and Adapted NOS. RESULTS: Twenty articles representing 18 unique studies were included in the narrative synthesis. Associations with lower late-life cognition were reported for: childhood maternal death, parental divorce, physical neglect, emotional neglect, physical abuse, and combinations of ACEs. However, most results were statistically nonsignificant, and many were unlikely to be clinically important. DISCUSSION AND IMPLICATIONS: We found an association between ACEs and late-life cognition. However, the direction and magnitude of association varied between and within types of ACEs and measures of cognitive function. Most included articles had a moderate risk of bias. This review is the first attempt to synthesize the literature on this topic and it outlines the next steps to improve the evidence base in the area.
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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 | 0.022 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".