Latent classes of oppositional defiant disorder in adolescence and prediction to later psychopathology
Bibliographic record
Abstract
Current conceptualizations of oppositional defiant disorder (ODD) place the symptoms of this disorder within three separate but related dimensions (i.e., angry/irritable mood, argumentative/defiant behavior, vindictiveness). Variable-centered models of these dimensions have yielded discrepant findings, limiting their clinical utility. The current study utilized person-centered latent class analysis based on self and parent report of ODD symptomatology from a community-based cohort study of 521 adolescents. We tested for sex, race, and age differences in the identified classes and investigated their ability to predict later symptoms of depression and conduct disorder (CD). Diagnostic information regarding ODD, depression, and CD were collected annually from adolescents (grades 6-9; 51.9% male; 48.7% White, 28.2% Black, 18.5% Asian) and a parent. Results provided evidence for three classes of ODD (high, medium, and low endorsement of symptoms), which demonstrated important developmental differences across time. Based on self-report, Black adolescents were more likely to be in the high and medium classes, while according to parent report, White adolescents were more likely to be in the high and medium classes. Membership in the high and medium classes predicted later increases in symptoms of depression and CD, with the high class showing the greatest risk for later psychopathology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".