Biological embedding of child maltreatment: A systematic review of biomarkers and resilience in children and youth.
Bibliographic record
Abstract
OBJECTIVE: Child maltreatment (CM) is a widespread problem associated with poor mental and physical health outcomes. The underlying mechanisms of this link are not always well understood, however certain biological changes observed in maltreated individuals may play a role in connecting experience and outcome. This review specifically focuses on 2 markers of biological embedding, DNA methylation (DNAm) and telomere length (TL) in maltreated children and youth. As biomarker changes are not uniform among maltreated children, we additionally discuss biological and environmental resilience factors that may contribute to variability. METHOD: We conducted a systematic review of Medline, Embase and PsycINFO databases for studies examining DNAm and/or TL in maltreated children and youth. Methodological quality of the included studies was assessed using the Scottish Intercollegiate Guidelines Network (SIGN) checklists for cohort studies and randomized control trials. Data extraction focused on various factors including population and CM (type, chronicity, severity, and duration) characteristics. RESULTS: The initial search returned 1,688 nonduplicate results, with 417 full text articles reviewed. Twenty-six articles from 16 studies were ultimately included of which 8 examined telomere length and 18 examined DNA methylation. CONCLUSIONS: While some heterogeneity of findings was found, evidence supports differential changes in both biomarkers associated with CM. This review enhances understanding of the constellation of biological changes related to CM and consideration of the important role of resilience factors in mitigating risk. Elucidating these factors may highlight targets for future study and intervention development. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".