Childhood maltreatment, cognitive performance, and cognitive decline in middle-aged and older adults with chronic disease: A prospective study
Bibliographic record
Abstract
OBJECTIVES: Childhood maltreatment (CM) may increase the risk for cognitive deficits and dementia later in life. However, most research has been cross-sectional in nature, has typically focused on specific types of CM, and rarely examined individual differences. The objectives are to evaluate 1) if CM predicts poorer cognitive performance and greater cognitive decline over a 5-year follow-up in older men and women with coronary artery disease (CAD) or other non-cardiovascular (non-CVD) chronic disease, and whether 2) sex and CAD status influence these relations. METHODS: Men and women (N = 1254; 39.6 % women; 65.6 ± 7.0 years old) with CAD or other non-CVD chronic diseases completed the Childhood Trauma Questionnaire Short Form (CTQ-SF). The Montreal Cognitive Assessment (MoCA) was administered twice at 5-year intervals. Main analyses included bivariate correlations, hierarchical analyses and moderation analyses controlling for sociodemographic and health parameters. RESULTS: CM was experienced by 32 % of the sample, while scores suggestive of cognitive deficits were obtained by 32.7 % and 40.2 % at study onset and follow-up, respectively. CM was associated with significantly lower MoCA scores at study onset (b = -0.013, p = 0.020), but not with change in MoCA over time (b = -0.002, p = 0.796). While MoCA scores did differ as a function of sex and CAD status, the latter did not influence the relations between maltreatment and MoCA. CONCLUSIONS: CM predicted poorer cognitive functioning among older individuals with chronic diseases but did not play a role in any further cognitive decline over the follow-up period. Further research is needed to help understand the mechanisms implicated.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".