Copy Number Variation Analysis of Aggressive Behaviour in Schizophrenia
Bibliographic record
Abstract
INTRODUCTION: An increased proclivity towards violence is often associated with those diagnosed with schizophrenia (SCZ), despite contradictory findings from prior studies exploring the association between violence and SCZ. Evidence has shown that certain comorbidities, specifically the presence of a substance use disorders, can result in increased aggression in those with SCZ. Copy number variation (CNV) load has also previously been implicated in the genetic vulnerability of individuals with SCZ. For this study, we aimed to determine whether CNV load correlates with increased violence in SCZ. METHODS: Community-dwelling patients diagnosed with SCZ spectrum disorders (n = 203) were recruited from a non-forensic population. The assessment for aggression was completed using a cross-sectional and retrospective design, and CNV analysis was conducted analysing genomic DNA using the Illumina Omni 2.5 array. RESULTS: No correlation between the number of CNV events (either deletion or duplication) and the severity of the physical violence episode index was found. However, there was a significant association between larger deletion events across the violent behaviours under investigation. DISCUSSION: These results need to be confirmed in more extensive studies using standardized tools developed for non-forensic populations, such as the Brown-Goodwin Scale of Aggression.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".