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Record W2102854376 · doi:10.1080/10550490050148080

Diagnostic Subgroups within a Sample of Comorbid Substance Abusers: Correlates and Characteristics

2000· article· en· W2102854376 on OpenAlex
Tony Toneatto, Juan Minango, Kim Calderwood

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal on Addictions · 2000
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsPublic Health OntarioUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsPsychiatryComorbidityAddictionAnxietyMoodPsychiatric comorbiditySubstance abuseMood disordersMental healthClinical psychologySubstance usePsychologyAlcohol use disorderMedicineAlcohol

Abstract

fetched live from OpenAlex

Patients seeking treatment at the Addiction Research Foundation for a substance problem but who also reported psychiatric symptomatology were referred to the Mental Health Unit. Following a clinical psychiatric interview, these patients were categorized into one of six diagnostic subgroups based on the presence of DSM-III-R psychiatric disorders: mood, anxiety, psychotic, organic, Axis-II, and adjustment. A control group of patients referred to the Mental Health Unit but not diagnosed with a psychiatric disorder was also included. These groups were compared on several demographic, substance use, and psychiatric variables. Patients assigned a diagnosis of organic (substance-induced) and Axis II disorders were found to have more severe substance use histories, alcohol-related consequences and longer treatment histories. Patients with a diagnosis of adjustment disorder appeared to be functioning relatively better. Implications of studying the heterogeneity of comorbidity are discussed.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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