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Record W137805992 · doi:10.1139/jpn.0305

Substance use and cognition in early psychosis

2003· article· en· W137805992 on OpenAlexaffvenue
Alissa Pencer, Jean Addington

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2003
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPsychosisCognitionPsychologyPsychiatryCognitive psychologyPsychotherapist

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the relation between substance use and cognition in individuals experiencing their first episode of psychosis. DESIGN: Prospective cross-sectional and longitudinal study. SETTING: An Early Psychosis Treatment and Prevention Program, an outpatient clinic in a psychiatry department at a university-affiliated hospital. PARTICIPANTS: Individuals with a psychotic illness who were admitted to an Early Psychosis Program; 266 patients were assessed at initial presentation, 159 at 1 year and 90 at 2 years. Most were outpatients. MEASURES: The effects of substance use (alcohol, cannabis, hallucinogens, cocaine, stimulants) on cognition were assessed. Substance use was determined by DSM-IV criteria, and the Case Manager Rating Scale was used to determine the level of substance use. A comprehensive cognitive battery of tests was used, and the Positive and Negative Syndrome Scale for schizophrenia was administered to all subjects to determine levels of positive and negative symptoms. RESULTS: Overall, both cross-sectionally and longitudinally, there were no significant associations between cognitive functioning and the use of various substances. Substance use was associated with higher positive symptoms. CONCLUSIONS: Individuals with psychotic disorders who show mild-to-moderate abuse of substances, in particular alcohol and cannabis, do not exhibit more cognitive impairment than those who do not do use the substances. However, substance use may have other detrimental effects on the process of the psychotic illness.

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 score0.003
Threshold uncertainty score0.174

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)0.0000.000

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 head0.040
GPT teacher head0.308
Teacher spread0.268 · 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

Citations123
Published2003
Admission routes2
Has abstractyes

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