Relationship Between Childhood Trauma and Psychotic Symptoms in Patients with Schizophrenia
Bibliographic record
Abstract
Introduction The association between childhood trauma and psychotic symptoms is still not clearly understood. Findings for positive and negative symptoms are confounding. This symptomatic response may differ according to the type of childhood trauma, for example childhood abuse was associated with positive symptoms while childhood neglect was associated with negative symptoms. Objectives This study examined the relationship between childhood trauma and psychotic symptoms in schizophrenic patients after controlling for the possible confounding factors, such as clinical features, depression, and sleep quality. Methods The childhood trauma questionnaire – short form, Positive and Negative Syndrome Scale (PANSS), Calgary Depression Scale for Schizophrenia, Pittsburgh sleep quality index, and the suicidality subscale of mini-international neuropsychiatric interview were administered to 199 patients with schizophrenia. We used sequential multiple stepwise regression analyses in which positive symptoms, negative symptoms, overall psychopathology and total symptoms of schizophrenia were dependent variables. Results Depressive symptomatology and childhood physical abuse (CPA) significantly contributed to positive, negative, general psychopathology and global schizophrenia symptomatology. Stepwise regression analysis results are presented in Table 1. Conclusions Our findings suggest that CPA during childhood could have an impact on psychopathology in schizophrenia. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".