Perspectives on Integrating Biological Assessments to Address the Health Effects of Childhood Adversities
Bibliographic record
Abstract
A majority of adults in the United States report a range of stressful and potentially traumatic childhood experiences (e.g., physical or sexual abuse, witnessing violence, neglect). Such adversities are associated with a range of mental (e.g., anxiety, mood, and behavioral difficulties) and physical (e.g., cardiovascular illnesses, diabetes, asthma) health problems. Increasingly, precision medicine approaches seek to prevent and treat such multifinal downstream health problems by identifying common etiological pathways (e.g., inflammation and immune pathways) and candidate biomarkers to target interventions. In this context, we review the rationale for continued research to identify biomarkers of childhood adversity. Building on the bioecological theory, we emphasize that individual neurobiological profiles develop within multiple ecological levels (individual, family, neighborhood, macrosocial) that confer both risk and protective factors that can attenuate or amplify biological effects of childhood adversity. Given the limited data on adversity-associated biomarkers for children and adolescents, we discuss future recommendations for research, implications for clinical care, and ethical considerations. Preventing childhood adversity and supporting adversity- and trauma-informed systemic intervention approaches remains our primary recommendation. We highlight the continued need to consider both biomarkers of risk and protective factors across ecological levels in future research.
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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 | 0.075 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".