Association Between Adverse Childhood Experiences Score and Traumatic Brain Injury Occurrence: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Adverse childhood experiences (ACEs) and traumatic brain injuries (TBI) are highly prevalent globally, and both are associated with long-term negative health outcomes across the lifespan. Past research exploring the potential association between ACEs and TBI occurrence has demonstrated mixed findings. Thus, we conducted a systematic review and meta-analysis to examine the association between the ACEs measure and TBI occurrence. Moderator analyses were conducted to determine whether certain factors, including participant age, sex, and geographical location, modified the association between ACEs score and TBI occurrence. Searches were conducted in PsycINFO, MEDLINE, Embase, and CINHAL for studies published between January 1, 1998, and February 19, 2024. A total of 42 full-text articles were screened against inclusion criteria (i.e., measure of ACEs using the original 8- or 10-item scale or another composite measure of ACEs, TBI occurrence, and effect size for the association between ACEs score and TBI). Eight studies and 10 samples ( N = 4954) were included in the meta-analysis. The data were synthesized using a random-effects multilevel meta-analysis, which revealed a significant large positive association between ACEs score and TBI occurrence, r = 0.31, 95% confidence interval [0.13, 0.49], p < 0.001. Moderator analyses did not yield significant results. The current findings demonstrate that individuals who reported a higher ACEs score were more likely to have reported sustaining a TBI, highlighting a need for trauma-informed efforts to prevent TBI and its adverse effects.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".