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Record W2109605674 · doi:10.3371/csrp.shjo.030813

Pathways to Psychosis in Cannabis Abuse

2013· review· en· W2109605674 on OpenAlexaff
Amresh Shrivastava, Megan Johnston, Kristen Terpstra, Y Bureau

Bibliographic record

VenueClinical Schizophrenia & Related Psychoses · 2013
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsWestern UniversityLawson Health Research InstituteUniversity of TorontoMental Health Research Canada
Fundersnot available
KeywordsCannabisPsychosisSchizophrenia (object-oriented programming)Effects of cannabisCausality (physics)PsychologyCannabinoidPsychiatryDelta-9-tetrahydrocannabinolTetrahydrocannabinolCognitionNeuroscienceClinical psychologyMedicineCannabidiol

Abstract

fetched live from OpenAlex

Cannabis has been implicated as a risk factor for the development of schizophrenia, but the exact biological mechanisms remain unclear. In this review, we attempt to understand the neurobiological pathways that link cannabis use to schizophrenia. This has been an area of great debate; despite similarities between cannabis users and schizophrenia patients, the evidence is not sufficient to establish cause-and-effect. There have been advances in the understanding of the mechanisms of cannabis dependence as well as the role of the cannabinoid system in the development of psychosis and schizophrenia. The neurobiological mechanisms associated with the development of psychosis and effects from cannabis use may be similar but remain elusive. In order to better understand these associations, this paper will show common neurobiological and neuroanatomical changes as well as common cognitive dysfunction in cannabis users and patients of schizophrenia. We conclude that epidemiologic evidence highlights potential causal links; however, neurobiological evidence for causality remains weak.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.680
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.008

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 head0.096
GPT teacher head0.433
Teacher spread0.337 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations13
Published2013
Admission routes1
Has abstractyes

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