Child Sexual Abuse and Age at Onset of Psychotic Disorders: A Matched-cohort Study: L’âge d’apparition des troubles psychotiques chez les victimes d’agression sexuelle à l’enfance: Une étude prospective de cohortes appariées
Bibliographic record
Abstract
Objective: Victims of child sexual abuse (CSA) present with a higher risk of psychotic disorders. However, the developmental course of psychosis following CSA, such as the age at onset, remains unknown. This study aimed to determine whether the age at onset of psychotic disorders was influenced by sexual abuse, sex, and confounding factors (substance misuse, intellectual disability, and socioeconomic status). Method: A prospective matched-cohort design was used, with administrative databases from a child protection agency (CPA) and a public health system. Children who received a substantiated report of CSA at the CPA and whose health data could be retrieved were selected ( n = 882) and matched with children from the general population using their date of birth, sex, and geographical area. Survival analysis was performed to estimate the association between sexual abuse, sex, and confounding factors and the age at onset of psychotic disorders. Results: Sexual abuse and substance misuse are significantly associated with the age at onset of psychotic disorders. In the sexually abused group, only substance misuse is associated with the age at onset of psychotic disorders, but this was not significant for the general population. Conclusions: These findings highlight the importance of prevention of psychotic disorders among sexually abused youth, especially those with a substance misuse diagnosis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".