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Record W4402242791 · doi:10.1111/jgh.16730

Clinical guidance for cannabidiol‐associated hepatotoxicity: A narrative review

2024· review· en· W4402242791 on OpenAlexaff
Lauren Eadie, Lindsay A. Lo, Michael Boivin, Jagpaul Kaur Deol, Caroline A. MacCallum

Bibliographic record

VenueJournal of Gastroenterology and Hepatology · 2024
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsBarrie Urology GroupQueen's UniversityUniversity of British Columbia
Fundersnot available
KeywordsCannabidiolMedicineNarrative reviewAdverse effectIntensive care medicineDrugLiver injuryDrug reactionMEDLINEAdverse drug reactionMedical literatureCannabisPharmacologyPsychiatryPathology

Abstract

fetched live from OpenAlex

There is increasing evidence that cannabidiol (CBD) use is associated with clinically significant liver enzyme (LE) elevations and drug-induced liver injury (DILI). The proportion of LE elevations and DILI events reported in the literature meet the Council for International Organizations of Medical Sciences' (CIOMS) classification of a common adverse drug reaction. However, these potential adverse events are unknown to many clinicians and may be overlooked. The increasing use of CBD for both medical and non-medical use necessitates clear direction in the diagnosis and management of CBD-associated hepatotoxicity. To our knowledge, no such clinical guidance currently exists. For people presenting with elevated LEs, CBD use should be screened for and be considered in the differential diagnosis. This narrative review will provide clinicians with guidance in the prevention, detection, and management of CBD-related hepatotoxicity.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.004
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.069
GPT teacher head0.452
Teacher spread0.383 · 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 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

Citations10
Published2024
Admission routes1
Has abstractyes

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