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Record W4223967886 · doi:10.1089/can.2022.0014

Expanding Research on Cannabis-Based Medicines for Liver Steatosis: A Low-Risk High-Reward Way Out of the Present Deadlock?

2022· article· en· W4223967886 on OpenAlexaff
Tangui Barré, Vincenzo Di Marzo, Fabienne Marcellin, Patrizia Burra, Patrizia Carrieri

Bibliographic record

VenueCannabis and Cannabinoid Research · 2022
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsNonalcoholic fatty liver diseaseEndocannabinoid systemSteatosisFatty liverMedicineClinical trialObservational studyCannabisIntensive care medicineBioinformaticsDiseasePsychiatryInternal medicineBiology

Abstract

fetched live from OpenAlex

Obesity and nonalcoholic fatty liver disease (NAFLD) constitute global and growing epidemics that result in therapeutic dead ends. There is an urgent need for new and accessible treatments to improve and widen both preventive and curative approaches against NAFLD. The endocannabinoid system (ECS) is recognized as a complex signaling apparatus closely related to metabolic disorders and is a key target for treating NAFLD. Despite a lack of conclusive clinical trials, observational and pre-clinical studies highlight putative benefits of phytocannabinoids on liver steatosis through multiple pathways. Owing to both its safety profile and its diversity of active compounds acting primarily (although not exclusively) on the ECS—and its expanded version, the endocannabinoidome, the Cannabis plant should be considered a major prospect in the treatment of NAFLD. However, seizing this opportunity, and intensifying clinical research in this direction, will require overcoming both scientific and nonscientific barriers.

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)0.013
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0040.003
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0010.000

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 head0.061
GPT teacher head0.374
Teacher spread0.313 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations6
Published2022
Admission routes1
Has abstractyes

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