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Record W3008359431 · doi:10.1037/pha0000354

Cannabis use, cognitive performance, and symptoms of attention deficit/hyperactivity disorder in community adults.

2020· article· en· W3008359431 on OpenAlexafffund
Tashia Petker, Jane DeJesus, Alex Lee, Jessica Gillard, Max M. Owens, Iris M. Balodis, Michael Amlung, Tony P. George, Assaf Oshri, Geoffrey B. Hall, Louis A. Schmidt, James MacKillop

Bibliographic record

VenueExperimental and Clinical Psychopharmacology · 2020
Typearticle
Languageen
FieldMedicine
TopicAttention Deficit Hyperactivity Disorder
Canadian institutionsUniversity of TorontoMcMaster University
FundersNational Institute on Alcohol Abuse and AlcoholismNational Institutes of HealthNational Institute on Drug AbusePeter Boris Centre for Addictions Research
KeywordsNeurocognitiveCannabisPsychologyCognitionAttention deficit hyperactivity disorderPsychiatryClinical psychologyPsycINFOEffects of sleep deprivation on cognitive performanceImpulsivityEffects of cannabisMEDLINE

Abstract

fetched live from OpenAlex

= 161). Age of first cannabis use was not significantly associated with any neurocognitive variables or ADHD symptomatology in all analyses. The current findings provide evidence of a link between current cannabis misuse and both hyperactive and inattentive ADHD symptoms in general, and possible links to attention and impulsive delay discounting in subgroups of cannabis users, but no associations in other cognitive domains or implication of earlier initiation of cannabis use in relation to cognitive performance or ADHD. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.037
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.048
GPT teacher head0.406
Teacher spread0.359 · 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 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

Citations17
Published2020
Admission routes2
Has abstractyes

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