Early trauma and schizophrenia onset: preliminary results of an outpatient cohort in Brazil
Bibliographic record
Abstract
Abstract Objectives To assess the prevalence of early trauma in individuals with onset of schizophrenia (SZ) at early (≤ 18 years) and adult (> 18 years) ages (EOP and AOP, respectively) and explore relationships between the onset of disease and clinical variables including traumatic events and psychotic and mood symptoms. Methods Subjects with SZ (n = 71) and EOP and AOP were compared for history of psychological trauma, sexual abuse, and physical punishment using the Early Trauma Inventory Self Report - Short Form (ETISR-SF). They were also compared for history of comorbidities and affective disorders using the Diagnostic Interview for Psychosis and Affective Disorders, the Positive and Negative Syndrome Scale, the Liebowitz Social Anxiety Scale, and the Calgary Depression Scale for Schizophrenia. Coefficients were calculated for correlations between scale results and disease duration. Results Early trauma was significantly associated with an early onset psychotic episode (r = -0.315, p < 0.01). General trauma and depressive symptoms in adulthood were also associated (r = 0.442, p < 0.01), as were social anxiety symptoms and early trauma (r = 0.319, p < 0.01). Total ETISR-SF scores and the physical abuse item were significantly higher in EOP than in AOP. In the hierarchical regression, PANSS scores were best predicted by a model including the duration of disease and age of first psychotic episode (R = 0.303). Conclusions Our results support the hypothesis that early trauma, including physical abuse, may play a relevant role in schizophrenia symptoms, such as an earlier psychotic occurrence, as well as features of other psychiatric disorders, such as greater severity of social anxiety and depression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".