Childhood Trauma May Increase Risk of Psychosis and Mood Disorder in Genetically High-risk Children and Adolescents by Enhancing the Accumulation of Risk Indicators
Bibliographic record
Abstract
Background: Genetically high-risk children carry indicators of brain dysfunctions that adult patients with schizophrenia or bipolar disorder display. The accumulation of risk indicators would have a higher predictive value of a later transition to psychosis or mood disorder than each individual risk indicator. Since more than 50% of adult patients report having been exposed to childhood trauma, we investigated whether exposure to trauma during childhood was associated with the early accumulation of risk indicators in youths at genetic risk. Methods: We first inspected the characteristics of childhood trauma in 200 young offspring (51% male) born to a parent affected by DSM-IV schizophrenia, bipolar disorder, or major depressive disorder. A subsample of 109 offspring (51% male) had measurements on four risk indicators: cognitive impairments, psychotic-like experiences, nonpsychotic nonmood childhood DSM diagnoses, poor global functioning. Trauma was assessed from direct interviews and reviews of lifetime medical and school records of offspring. Results: Trauma was present in 86 of the 200 offspring (43%). The relative risk of accumulating risk indicators in offspring exposed to trauma was 3.33 (95% CI 1.50, 7.36), but more pronounced in males (RR = 4.64, 95% CI 1.71, 12.6) than females (RR = 2.01, 95% CI 0.54, 7.58). Conclusion: Childhood trauma would be related to the accumulation of developmental precursors of major psychiatric disorders and more so in young boys at high genetic risk. Our findings may provide leads for interventions targeting the early mechanisms underlying the established relation between childhood trauma and adult psychiatric disorders.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".