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Record W3028567414 · doi:10.1093/schbul/sbaa028.053

O9.6. SYSTEMATIC REVIEW AND META-ANALYSIS OF THE EFFECTS OF CANNABIS USE IN ADOLESCENCE ON IQ IN LONGITUDINAL STUDIES ACCOUNTING PRE-EXPOSURE BASELINE PERFORMANCE

2020· article· en· W3028567414 on OpenAlexaboutno aff
Emmet Power, Sophie Sabherwal, Aisling O’Neill, Colm Healy, David Cotter, Mary Cannon

Bibliographic record

VenueSchizophrenia Bulletin · 2020
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisPsychiatryPsychologySchizophrenia (object-oriented programming)DementiaClinical psychologyPopulationCognitive skillMeta-analysisMEDLINEBorderline intellectual functioningCognitionMedicineDiseaseInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Cannabis use in adolescence is a known risk factor for developing schizophrenia. Decline in intellectual functioning is a well-studied phenomenon of schizophrenia. Schizophrenia in its first classification was conceptualized as a dementia - ‘Dementia Praecox’ indicating a dominance of effect of cognitive symptoms on functioning at that time. First episode psychosis patients with histories of cannabis use compared to those without cannabis use have superior cognitive functioning suggesting different disease phenotypes. Our aim was to investigate whether cannabis had an effect on full scale IQ in general population samples to further inform understanding of this disease pathway. Methods We developed a search strategy through an iterative approach with a qualified information specialist. We searched three databases: Medline, Embase and PsychInfo. We included conference abstracts and full text publications in English. We contacted authors for additional information in cases where an effect size was not calculable. We included longitudinal studies of non-help-seeking young people in the community with a pre-drug exposure standardized measure of IQ prior to the age of 18 and a comparable measure at subsequent follow up. We defined the case group as individuals with a history of heavy cannabis use (more than 25 lifetime uses, at least weekly use for 6 months and/or meeting criteria for a cannabis use disorder) and the control group as similar young people who had no or very minimal experimental exposure to cannabis (<5 lifetime uses). Two reviewers independently extracted the data and assessed for bias using the Newcastle-Ottawa risk of bias tool. We performed random effects meta-analysis in Stata version 14 given expected heterogeneity in measurement of cannabis use across studies. Results We identified 7 cohort studies detailed in 6 reports including over 650 subjects and 5600 controls. Study designs included twin cohorts, representative cluster sampled community cohorts and birth cohort studies. Range of follow up was between 2 and 23 years. All studies showed relative decline in IQ of which two were statistically significant. Our findings show that cannabis use in youth is associated with modest IQ differences that equate to approximately to a 2-point decrease in young cannabis users. Studies examining twin pairs showed that twins discordant for cannabis use did not have divergent cognitive trajectories, however these analyses were relatively underpowered to find an effect. Discussion Our findings demonstrate the harmful effects of cannabis use on brain development in young people. These findings are of public health importance and provide further evidence for the detrimental effects of early cannabis use on mental health and cognition.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.057
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.057
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0210.032
Bibliometrics0.0100.011
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.286
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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