Machine-learning of medical cannabis chemical profiles reveals analgesia beyond placebo expectations.

ABSTRACT:

Background

The efficacy of medical cannabis in alleviating pain has been demonstrated in clinical trials, yet questions remain regarding the extent to which specific chemical compounds contribute to analgesia versus expectation-based (placebo) responses. Effective blinding is notoriously difficult in cannabis trials, complicating the identification of compound-specific effects.

Methods

In a prospective study of 329 chronic pain patients (40% females; aged 48.9 ± 15.5) prescribed medical cannabis, we examined whether the chemical composition of cannabis cultivars could predict treatment outcomes. We used a Random Forest classifier with nested cross-validation to assess the predictive value of demographics, clinical features, and approximately 200 chemical compounds. Model rob

SUBMITTER: Hatav A 

PROVIDER: S-EPMC12267634 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature