Two‐year predictive validity of conduct disorder subtypes in early adolescence: a latent class analysis of a Canadian longitudinal sample
Bibliographic record
Abstract
BACKGROUND: Investigating the latent structure of conduct disorder (CD) can help clarify how symptoms related to aggression, property destruction, theft, and serious violations of rules cluster in individuals with this disorder. Discovering homogeneous subtypes can be useful for etiologic, treatment, and prevention purposes depending on the qualitative or quantitative nature of the symptomatology. The aim of the present study is twofold: identify subtypes of CD in young adolescents based on latent class analysis (LCA) and investigate the two-year predictive validity of CD subtypes on deviant and criminal lifestyles. METHODS: Adolescent-reported CD symptoms were collected using the National Longitudinal Survey of Children and Youth. Three cohorts of 12-13-year-olds were assessed during 1994-1995, 1996-1997, and 1998-1999 (N = 4,125). RESULTS: Latent class analyses yielded 4 distinct subtypes: No CD (82.4%); Non-Aggressive CD ('NACD', 13.9%); Physically Aggressive CD ('PACD', 2.3%); and Severe-Mixed CD ('SMCD', 1.4%). Predictive validity at age 14-15 was non-specific, although the SMCD type had, by far, the highest odds of deviant and criminal lifestyle outcomes in comparison to youth with PACD or NACD. NACD and PACD had similar odds of deviant outcomes, even if most NACD youth were subthreshold CD (fewer than three symptoms). CONCLUSION: In early adolescence, CD is qualitatively and quantitatively heterogeneous, suggesting multiple developmental pathways. However, they appear to predict similarly violent and non-violent outcomes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".