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Record W4237676457 · doi:10.1081/ada-100104512

ANTISOCIAL BEHAVIORAL SYNDROMES AND RETURN TO DRUG USE FOLLOWING RESIDENTIAL RELAPSE PREVENTION/HEALTH EDUCATION TREATMENT

2001· article· en· W4237676457 on OpenAlexaff
Risë B. Goldstein, Carol Bigelow, Jane McCusker, Benjamin F. Lewis, Kenneth A. Mundt, Sally I. Powers

Bibliographic record

VenueThe American Journal of Drug and Alcohol Abuse · 2001
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsMcGill UniversitySt Mary's Hospital Centre
FundersNational Institute on Drug AbuseUniversity of Massachusetts Amherst
KeywordsAntisocial personality disorderRecidivismAddictionPsychiatryPsychologyClinical psychologySubstance abuseDrugRelapse preventionConduct disorderYoung adultComorbidityMedicineInjury preventionPoison controlDevelopmental psychologyEmergency medicine

Abstract

fetched live from OpenAlex

This study compared residential addiction treatment clients meeting full DSM-III-R criteria for antisocial personality disorder (ASPD) with those reporting syndromal levels of antisocial behavior only in adulthood (AABS) on time to and severity of first posttreatment drug use. Antisocial syndrome and selected other mental disorders were assessed using the Diagnostic Interview Schedule, Revised for DSM-III-R, and validity of self-reported posttreatment drug behavior was measured against results of hair analysis. Among subjects followed within 180 days after treatment exit, individuals with ASPD were at modestly increased risk of a first lapse episode compared to those with AABS. However, the two groups did not differ in severity of lapse. Participants with ASPD demonstrated poorer agreement between self-reported posttreatment drug behavior and hair data. These results add to the evidence suggesting that the DSM requirement for childhood onset in ASPD may be clinically important among substance abusers in identifying a severely antisocial and chronically addicted group at elevated risk for early posttreatment recidivism. Our findings support the importance of careful classification of antisocial syndromes among substance abusers and the identification of characteristics of these syndromes that underlie clients' risks for posttreatment return to drug use to provide optimally individualized treatment planning.

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)0.000
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 score0.090
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.039
GPT teacher head0.359
Teacher spread0.321 · 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

Citations19
Published2001
Admission routes1
Has abstractyes

Explore more

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