Admissions for psychosis following agency-notified child maltreatment at 40-year-follow-up: Results from the Childhood Adversity and Lifetime Morbidity (CALM) cohort
Bibliographic record
Abstract
There is substantial evidence of an association between self-reported child maltreatment (CM) and subsequent psychosis in retrospective data. Such findings may be affected by recall bias. Prospective studies of notifications to statutory agencies address recall bias but are less common and subject to attrition bias. These studies may therefore be underpowered to detect significant associations for some CM types such as sexual abuse. This study therefore linked administrative health data to a large birth cohort that included notifications to child protection agencies. We assessed psychiatric outcomes of CM as measured by inpatient admissions for non-affective psychoses (ICD10 codes F20-F29) to both public and private hospitals in Brisbane, Australia. Follow-up was up to 40 years old. There were 6087 cohort participants whose data could be linked to the administrative health data. Of these, 10.1 % had been the subject of a CM notification. Seventy-two participants (1.2 %) had been admitted for non-affective psychosis by 40-year follow-up. On adjusted analysis, all notified and substantiated types of CM were associated with admissions for non-affective psychosis. This included neglect, physical, sexual or emotional abuse, as well as notifications for multiple CM types. For instance, there was a 2.72-fold increase in admissions following any agency notification (95 % CI = 1.53-4.85). All maltreatment types therefore show a significant association with subsequent admissions for psychosis up to the age of 40. Screening for CM in individuals who present with psychosis is, therefore, indicated, as well as greater awareness that survivors of CM may be at higher risk of developing psychotic symptoms.
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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) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".