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Record W4234937125 · doi:10.1521/pedi_2012_26_074

Overt Versus Covert Conduct Disorder Symptoms and the Prospective Prediction of Antisocial Personality Disorder

2014· article· en· W4234937125 on OpenAlexaffabout
Yann Le Corff, Jean Toupin

Bibliographic record

VenueJournal of Personality Disorders · 2014
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPsychologyConduct disorderAntisocial personality disorderCovertProspective cohort studyLogistic regressionClinical psychologyImpulsivityPsychiatryPoison controlInternal medicineInjury preventionMedicine

Abstract

fetched live from OpenAlex

Studies have shown strong continuity between conduct disorder (CD) in adolescence and antisocial personality disorder (APD) in adulthood. Researchers have been trying to explain why some adolescents with CD persist into adult APD and others do not. A few studies reported that overt and covert CD symptoms have a differential predictive power for APD, with mixed results. The present study aimed to evaluate the prospective association of overt and covert CD symptoms with APD in a sample of male adolescents with CD (N = 128, mean age = 15.6, SD = 1.6). Participants were recruited at intake in Quebec Youth Centers and reassessed 3 years later (n = 73). CD and ADHD symptoms were assessed at intake with the DISC-R while APD was assessed 3 years later with the SCID-II. Logistic regression results showed that, contrary to previous prospective studies (Lahey, Loeber, Burke, & Applegate, 2005; Washburn et al., 2007), overt (OR = 2.12, 95% CI [1.29, 3.50]) but not covert (OR = 1.04, 95% CI [0.69, 1.56]) symptoms predicted later APD, controlling for ADHD symptoms and socioeconomic status. It is hypothesized that the divergence with previous studies may be explained by the higher mean number and wider range of overt CD symptoms in our sample.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.285
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2014
Admission routes2
Has abstractyes

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