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Record W7115359401

A six-hour time series of the acute effects of ingested cannabis intoxication on a battery of cognitive tasks

2024· article· en· W7115359401 on OpenAlexaboutno aff

Bibliographic record

VenueMacedonian Journal of Medical Sciences (University of Skopje) · 2024
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisCognitionEffects of cannabisIngestionEffects of sleep deprivation on cognitive performancePoison controlInhalationTetrahydrocannabinol
DOInot available

Abstract

fetched live from OpenAlex

As ≥33% of Canadian adults used cannabis last year, characterizing the effects of acute cannabis intoxication on cognitive function is imperative. The effects of inhaled cannabis intoxication on several cognitive tasks are well known; however, ≥50% of cannabis users consumed ingestible cannabis products (i.e., ‘edibles’) in 2023, for which the cognitive effects are poorly understood. Edible intoxication likely presents differently from inhalation due to different absorption and metabolic routes for the psychoactive component of cannabis, Δ9-tetrahydrocannabinol (THC). Thus, the purpose of our study was to characterize the acute effects of ingested cannabis intoxication on cognitive function. Fourteen participants (8 female; 29.7±7.3years), who had consumed cannabis before, but not more than once per week in the six months prior to participation, performed a battery of cognitive tasks before (baseline; BL) as well as immediately (0h), 0.5h, 1h, 1.5h, 2h, 2.5h, 3h, 3.5h, 4h, 5h, and 6h following ingestion of two 5mg THC capsules. The battery included simple reaction time (SRT), go/no go reaction time (GNG), Corsi block tapping (CBT), visuospatial trail making (VTM), and a subjective questionnaire. Outcomes at each timepoint were compared to BL. Participants felt intoxicated 1-6h post-ingestion. SRT and SRT variability were increased from 2.5-3.5h and 3-4h, respectively. GNG accuracy was reduced from 1h-6h, and VTM errors were greater at 2h (all p

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.275
Teacher spread0.264 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueMacedonian Journal of Medical Sciences (University of Skopje)Same topicCannabis and Cannabinoid ResearchFrench-language works237,207