Relationship Between Adverse Childhood Experience Survey Items and Psychiatric Disorders
Bibliographic record
Abstract
CONTEXT: Developmental psychopathology theory suggests a relationship between early childhood adversity and mental disorder. OBJECTIVE: To examine the relationship between the specific items on the Adverse Childhood Experiences (ACE) survey and the International Classification of Diseases, Tenth Revision (ICD-10) categories of psychiatric diagnoses in a pediatric sample. DESIGN: The sample included patients enrolled in the Child and Adolescent Addiction Mental Health and Psychiatry Program with both a completed ACE survey and at least 1 diagnosis of record (per admission). These criteria yielded 2 samples for each sex (ACE survey item frequencies and values in collapsed and multiple-admission groups). Data were analyzed employing tetrachoric correlation, hierarchical regression, and polychoric factor analysis. RESULTS: Hierarchical regression analysis identified that ICD-10 diagnostic categories, except for substance disorders, were not consistently related to ACE total score and tended to reduce the magnitude of the ACE total score in the multiple-admission group. Tetrachoric correlation revealed very low (< 0.4) positive and negative correlations between ICD-10 categories and ACE items in both multiple-admission and collapsed sample groups. Polychoric factor analysis indicated that the ACE survey items and the ICD-10 categories for both sexes were independent, with only the diagnostic ICD-10 category substance disorders being marginally associated with the ACE items factor for females. CONCLUSION: The nominal relationship between ACE items and ICD-10 diagnostic categories indicates the need to include ACE assessment in advance of differential diagnosis and implementation of conventional mental health interventions for children and adolescents.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.003 | 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".