ArticleCommunications medicine2025
Machine-learning of medical cannabis chemical profiles reveals analgesia beyond placebo expectations.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Predicting of early miscarriage based on machine learning: a retrospective cohort study in China.BMC pregnancy and childbirth · 2026Article
- Cannabinoid efficacy for pain: dose, chronicity and route.Frontiers in neuroscience · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsIn 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 robustness was evaluated using six additional machine learning algorithms.
resultsHere we show that incorporating chemical composition markedly improves the prediction of pain relief (AUC = 0.63 ± 0.10) compared to models using only demographic and clinical features (AUC = 0.52 ± 0.09; p < 0.001). This result is consistent across all models tested. While well-known cannabinoids such as THC and CBD provide limited predictive value, specific terpenoids, particularly α-Bisabolol and eucalyptol, emerge as key predictors of treatment response.
conclusionsOur findings demonstrate that pain relief can be predicted from cannabis chemical profiles that are unknown to patients, providing evidence for compound-specific therapeutic effects. These results highlight the importance of considering the full range of cannabis compounds when developing more precise and effective cannabis-based therapies for pain management.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.