Cannabidiol‐associated hepatotoxicity: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Findings of liver enzyme elevations in recent cannabidiol studies have raised concerns over liver safety. This study aimed to determine the association between cannabidiol use, liver enzyme elevation, and drug-induced liver injury (DILI). METHODS: In this systematic review and meta-analysis, a search of EMBASE, CENTRAL, CINAHL, Clinicaltrials.gov, Medline, medRxiv, and Web of Science of records up to February 2022 was conducted. Clinical trials initiating daily cannabidiol treatment with serial liver enzyme measures were included. The proportion of liver enzyme elevations and DILI were independently extracted from published reports. Pooled proportions and probability meta-analyses were conducted. RESULTS: Cannabidiol use was associated with an increased probability of liver enzyme elevation (N = 12 trials, n = 1229; OR = 5.85 95% CI = 3.84-8.92, p < 0.001) and DILI (N = 12 trials, n = 1229; OR = 4.82 95% CI = 2.46-9.45, p < 0.001) compared to placebo controls. In participants taking cannabidiol (N = 28 trials, n = 1533), the pooled proportion of liver enzyme elevations was 0.074 (95% CI 0.0448-0.1212), and DILI was 0.0296 (95% CI 0.0136-0.0631). High-dose CBD (≥1000 mg/day or ≥20 mg/kg/day) and concomitant antiepileptic drug use were identified as risk factors. No cases were reported in adults using cannabidiol doses <300 mg/day. No cases of severe DILI were reported. CONCLUSIONS: Cannabidiol-associated liver enzyme elevations and DILI meet the criteria of common adverse drug events. Clinicians are encouraged to screen for cannabidiol use and monitor liver function in patients at increased risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".