Retrospectively assessed childhood trauma experiences are associated with illness severity in mental disorders adjusted for symptom state
Bibliographic record
Abstract
Converging evidence suggests that childhood trauma is a causal factor in schizophrenia (SZ) and in bipolar disorders (BD). Here, we investigated whether retrospective reports are associated with severity of illness, independent of current symptom state in a large sample of participants with SZ or BD. We included 1260 individuals (SZ [n = 461], BD [n = 352]), and healthy controls; HC [n = 447]) recruited from the same catchment area. A history of childhood trauma was obtained with the Childhood Trauma Questionnaire (CTQ). Diagnosis and episodes were obtained with the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I). Clinical symptoms (state) were assessed with the Positive and Negative Syndrome scale (PANSS), the Calgary Depression Scale (CDSS). Trait related illness characteristics were assessed with age at illness onset, number of episodes, and lifetime suicide attempts. Patients who reported multiple types of childhood trauma experiences had significantly more severe illness course including an earlier illness onset, more mood episodes, and increased risk of at least one suicide attempt, also after adjusting for current symptom state. Retrospective assessed childhood trauma experiences are associated with illness severity in mental disorders adjusted for symptom state. Our results strengthen the role of childhood trauma in development of psychopathology.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".