We report a systematic review and meta-analysis of studies which assessed the antinociceptive efficacy of cannabinoids, cannabis-based medicines, and endocannabinoid system modulators on pain-associated behavioural outcomes in animal models of pathological or injury-related persistent pain. In April 2019, we systematically searched 3 online databases and used crowd science and machine learning to identify studies for inclusion. We calculated a standardised mean difference (SMD) effect size for each comparison and performed a random effects meta-analysis. We assessed the impact of study design characteristics and reporting of mitigations to reduce the risk of bias. We meta-analysed 374 studies in which 171 interventions were assessed for antinociceptive efficacy in rodent models of pathological or injury-related pain. Most experiments were conducted in male animals (86 %). Antinociceptive efficacy was most frequently measured by attenuation of hypersensitivity to evoked limb withdrawal. Selective CB₁, CB₂, non-selective cannabinoid receptor agonists (including delta-9-tetrahydrocannabinol; THC), and PPAR-alpha agonists (predominantly palmitoylethanolamide; PEA) significantly attenuated pain-associated behaviours in a broad range of inflammatory and neuropathic pain models. Fatty acid amide hydrolase (FAAH) inhibitors, monoacylglycerol lipase (MGL) inhibitors and cannabidiol (CBD) significantly attenuated pain-associated behaviours in neuropathic pain models but yielded mixed results in inflammatory pain models. The reporting of criteria to reduce the risk of bias was low, therefore the studies have an unclear risk of bias. The value of future studies could be enhanced by improving the reporting of methodological criteria, the clinical relevance of the models and behavioural assessments. Notwithstanding, the evidence supports the hypothesis of cannabinoid-induced analgesia.

Telehealth has emerged as a promising healthcare delivery modality due to its ability to ameliorate traditional access-level barriers to treatment. In response to the onset of the novel coronavirus (COVID-19) pandemic, multidisciplinary pain clinics either rapidly built telehealth infrastructure from the ground up or ramped up existing services. As the use of telehealth increases, it is critical to develop data collection frameworks that guide implementation. This applied review provides a theoretically-based approach to capitalize on existing data sources and collect novel data to inform virtually delivered care in the context of pediatric pain care. Reviewed multisource data are (1) healthcare administrative data; (2) electronic chart review; (3) clinical health registries; and (4) stakeholder feedback. Preliminary telehealth data from an interdisciplinary pediatric chronic pain management clinic (PPMC) serving youth ages 8–17 years are presented to illustrate how relevant implementation outcomes can be extracted from multisource data. Multiple implementation outcomes were assessed, including telehealth adoption rates, patient clinical symptoms, and mixed-method patient-report telehealth satisfaction. This manuscript provides an applied roadmap to leverage existing data sources and incorporate stakeholder feedback to guide the implementation of telehealth in pediatric chronic pain settings through and beyond COVID-19. Strengths and limitations of the modeled data collection approach are discussed within the broader context of implementation science.