Oxaliplatin, a key drug in the treatment of colorectal cancer (CRC), can cause oxaliplatin-induced peripheral neuropathy (OIPN) in a dose-dependent manner. These symptoms can severely affect daily life, and chronic OIPN often limits treatment continuation because of its correlation with the cumulative dose of oxaliplatin. Currently, effective preventive measures are unavailable. However, surgical glove compression therapy may reduce paclitaxel-induced neuropathy, suggesting its potential in preventing OIPN.

This multicentre, randomised, open-label, phase II/III trial evaluates surgical glove compression therapy to investigate the possible preventive effects of OIPN in patients with CRC receiving adjuvant capecitabine plus oxaliplatin chemotherapy. Patients with stage III CRC undergoing curative surgery will be enrolled and randomised into two groups. The intervention group will wear two layers of tight-fitting surgical gloves from 30 min before to 30 min after oxaliplatin infusion, whereas the control group will receive standard care. The primary endpoint is the incidence of grade ≥2 chemotherapy-induced peripheral neuropathy (CIPN) based on the Common Terminology Criteria for Adverse Events criteria. Secondary endpoints include quality of life assessments (Functional Assessment of Cancer Therapy/Gynecological Oncology Group-Neurotoxicity-12 and European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Chemotherapy-Induced Peripheral Neuropathy 20-item), duration and extent of OIPN as assessed using the Debiopharm Neurologic and Sensory Toxicity Criteria, chemotherapy completion rates, and adverse events. To detect a significant reduction in the incidence of CIPN, 170 patients will be enrolled (36% in the control group vs 15% in the intervention group). The planned case enrolment period is from 1 November 2024 to 31 October 2026.

This trial was approved by the Institutional Review Board of Hiroshima University, Japan (approval no. CRB2024-0008), and has been registered with the Japan Registry of Clinical Trials (jRCTs062240066). The results of this study will be submitted for publication in a peer-reviewed journal and shared with the scientific community at international conferences.

jRCTs062240066

Although artificial intelligence (AI) has been widely applied to electronic health record (EHR) data in hospital environments, its use in long-term care (LTC) facilities remains unexplored. Limited information technology infrastructure and unique challenges in LTC settings require a comprehensive examination of AI’s potential to enhance care quality and operational efficiency. With the aim of examining the application of AI to EHR data in LTC facilities, this scoping review will identify current AI applications for EHR in LTC, informing future research and potential care improvements in LTC settings.

This review will follow the scoping review methodological guidelines. The protocol of this scoping review has been registered on the Open Science Framework. The inclusion criteria are EHR (participants), AI (concept) and LTC facilities (context), with no date restrictions, but limited to articles published in English. Studies of any design focusing on AI applications for EHR in LTC settings will be considered. A systematic search will be performed on MEDLINE (Ovid), CINAHL (EBSCOhost), the Cochrane Central Register of Controlled Trials (Ovid), the Cochrane Database of Systematic Reviews (Ovid) and SCOPUS (Elsevier) by an information specialist. Two reviewers will independently screen titles and abstracts for inclusion based on predefined criteria. The same process will be repeated for full-text screening. Discrepancies will be resolved through team meetings with the third, fourth and fifth reviewers. All reasons for exclusion at the full-text stage will be documented and reported, with any discrepancies resolved by a review team.

As the data will be collected from existing literature, ethical approval is not required. The findings will be disseminated through conference presentations and publication in a peer-reviewed journal. The results will map current knowledge on AI applications in LTC facilities, thereby providing a foundation for future research aimed at enhancing the implementation and effectiveness of AI technologies in such settings.