BACKGROUND: Interest in and use of electronic consent (e-consent) in the conduct of academic clinical trials has increased since the COVID-19 pandemic. E-consent offers advantages including increased efficiency and accessibility, and reduced burden on site staff, which can be appealing to academic trialists anticipating challenges in recruitment to complex trial designs or with limited funding. However, there are many options to consider when using e-consent in a study protocol. This paper presents five case studies from Norwich Clinical Trials Unit, demonstrating how e-consent models can be effectively tailored to the needs of different trials. These examples illustrate the options around and benefits of e-consent, the acceptability of e-consent by participants, and the design considerations that were made during the development of the trial protocols.
METHODS: Five randomised trials are presented, selected from a range of different trial designs, disease areas, interventions, and patient populations. E-consent was either offered as an alternative to paper consent, according to participant preference, or as the sole method of consent. E-consent was generally used to facilitate remote consent in decentralised trials but was also chosen to increase efficiency and reduce burden in an emergency department setting. The technical implementation of e-consent and detailed participant procedures were tailored to the needs of the trial settings and patient populations. For example, accompanying participant information sheets were provided in paper or electronic form, and electronic signatures could be typed or drawn. Administrative data on uptake of e-consent is presented where available.
CONCLUSIONS: This paper demonstrates that the operational and technical aspects of implementing e-consent in clinical trials can be influenced by the trial design, the needs and characteristics of the trial population, financial/efficiency considerations, and level of risk. E-consent is not a one-size-fits-all tool for trials, and its use should be carefully considered during the development of the trial protocol, in conjunction with patient and public involvement contributors, site staff and other trial stakeholders.

Prior research highlights that rural populations have been historically underrepresented/excluded from clinical research. The primary objective of this study was to describe the inclusion of rural populations within our research enterprise using Clinical Research Management System demographic information at a large academic medical center in the Southeast. This was a cross-sectional study using participant demographic information for all protocols entered into our Clinical Research Management System between May 2018 and March 2021. Descriptive statistics were used to analyze the representation of rural and non-rural participants and demographic breakdown by age, sex, race, and ethnicity for our entire enterprise and at the state level. We also compared Material Community Deprivation Index levels between urban and rural participants. Results indicated that 19% of the research population was classified as rural and 81% as non-rural for our entire sample, and 17.5% rural and 82.5% urban for our state-level sample. There were significant differences in race, sex, and age between rural and non-rural participants and Material Community Deprivation Indices between rural and non-rural participants. Lessons learned and recommendations for increasing the inclusion of rural populations in research are discussed.

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UNASSIGNED: To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports.
UNASSIGNED: Data pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation.
UNASSIGNED: For THA, there were 5,558 patient radiology reports included, of which 4,137 were used for model training and testing, and 1,421 for external validation. Following training, model performance demonstrated average (mean across three folds) accuracy, F1 score, and area under the receiver operating curve (AUROC) values of 0.850 (95% confidence interval (CI) 0.833 to 0.867), 0.813 (95% CI 0.785 to 0.841), and 0.847 (95% CI 0.822 to 0.872), respectively. For TKA, 7,457 patient radiology reports were included, with 3,478 used for model training and testing, and 3,152 for external validation. Performance metrics included accuracy, F1 score, and AUROC values of 0.757 (95% CI 0.702 to 0.811), 0.543 (95% CI 0.479 to 0.607), and 0.717 (95% CI 0.657 to 0.778) respectively. There was a notable deterioration in performance on external validation in both cohorts.
UNASSIGNED: The use of routinely available preoperative radiology reports provides promising potential to help screen suitable candidates for THA, but not for TKA. The external validation results demonstrate the importance of further model testing and training when confronted with new clinical cohorts.

Robotic Roux-en-Y gastric bypass (RRYGB) is an innovative alternative to traditional laparoscopic approaches. Literature has been published investigating its safety/efficacy; however, the quality of reporting is uncertain. This systematic review used the Idea, Development, Exploration, Assessment and Long-term follow-up (IDEAL) framework to assess the reporting quality of available literature. A narrative summary was formulated, assessing how comprehensively governance/ethics, patient selection, demographics, surgeon expertise/training, technique description and outcomes were reported. Forty-seven studies published between 2005 and 2024 were included. There was incomplete/inconsistent reporting of governance/ethics, patient selection, surgeon expertise/training and technique description, with heterogenous outcome reporting. RRYGB reporting was poor and did not align with IDEAL guidance. Robust prospective studies reporting findings using IDEAL/other guidance are required to facilitate safe widespread adoption of RRYGB and other surgical innovations.

OBJECTIVE: Real-world evidence (RWE)-derived from analysis of real-world data (RWD)-has the potential to guide personalized treatment decisions. However, because of potential confounding, generating valid RWE is challenging. This study demonstrates how to responsibly generate RWE for treatment decisions. We validate our approach by demonstrating that we can uncover an existing adjuvant chemotherapy (ACT) guideline for stage II and III colon cancer (CC)-which came about using both data from randomized controlled trials and expert consensus-solely using RWD.
METHODS: Data from the population-based Netherlands Cancer Registry from a total of 27,056 patients with stage II and III CC who underwent curative surgery were analyzed to estimate the overall survival (OS) benefit of ACT. Focusing on 5-year OS, the benefit of ACT was estimated for each patient using G-computation methods by adjusting for patient and tumor characteristics and estimated propensity score. Subsequently, on the basis of these estimates, an ACT decision tree was constructed.
RESULTS: The constructed decision tree corresponds to the current Dutch guideline: patients with stage III or stage II with T stage 4 should receive surgery and ACT, whereas patients with stage II with T stage 3 should only receive surgery. Interestingly, we do not find sufficient RWE to conclude against ACT for stage II with T stage 4 and microsatellite instability-high (MSI-H), a recent addition to the current guideline.
CONCLUSIONS: RWE, if used carefully, can provide a valuable addition to our construction of evidence on clinical decision making and therefore ultimately affect treatment guidelines. Next to validating the ACT decisions advised in the current Dutch guideline, this paper suggests additional attention should be paid to MSI-H in future iterations of the guideline.

This case challenges the conventional preference for coronary artery bypass grafting (CABG) over percutaneous coronary intervention (PCI) in patients with diabetes, left main coronary artery disease (LMCAD) and multivessel disease. Current guidelines generally recommend CABG, especially in the context of LMCAD. However, our case involves a male patient with diabetes with LMCAD and extensive multivessel disease who was successfully treated with PCI, demonstrating a favorable outcome. Despite the high-risk profile, including a SYNTAX score of 28, the PCI approach was selected. This decision was supported by evidence suggesting comparable outcomes between PCI and CABG in similar patients. Our case highlights the potential of PCI as not just a viable, but potentially superior alternative in specific high-risk patients with diabetes, contrary to the prevailing belief in favor of CABG for all patients with left main involvement.

Chimeric antigen receptor T cell (CAR-T) therapies targeting the CD19 antigen have been associated with high and durable response rates in patients with diffuse large B cell lymphoma (DLBCL). CAR-T cell therapies are commonly administered in the inpatient setting due to the average onset of cytokine release syndrome within the first 3 days post infusion, but there has been growing interest in delivering CAR-T cell therapies in the outpatient setting to overcome frequent hospital bed shortages and the high cost of inpatient care. Although this approach could improve access whilst catering to patient preference, it requires a multidisciplinary approach as well as careful patient selection. Herein, Dr. Foley and Dr. Kuruvilla discuss the case of a patient presenting with the ideal profile for CAR-T cell therapy referral whilst also determining the key attributes for eligibility from a clinician\'s perspective. Solutions for successful outpatient management include proper education, caregiver support, and early referral to ensure a timely infusion. In conclusion, outpatient administration of CAR-T cell therapy in patients with DLBCLs should be assessed on a case-by-case basis.A vodcast feature is available for this article.

Community-engaged research often poses challenges due to exactly those qualities that make it desirable: it provides a new model of research that differs in many ways from top-down, university-led, prospectively designed approaches. While many have discussed the challenges to conducting community-engaged research, few have provided precise and generalizable lessons for how to surmount these challenges. Here we discuss the challenges experienced in a project that was community-engaged at three levels: 1) a research team consisting of an academic and a community partner as well as a community and academic research assistant, 2) the research team engaged with a Community Advisory Board called the CBOP-CERB (Community Based Organization Partners-Community Ethics Research Board) throughout the project, and 3) the research involved recruiting community participants from an area with a historical distrust of researchers and research: Flint Michigan. We also discuss administrative challenges that this multilevel community-engagement posed. Most important, we provide practical lessons in order for future community-engaged research to avoid or mitigate many of these challenges.

Providers have intended and unintended responses to payment reforms, such as China\'s new case-based payment system, i.e. Diagnosis-Intervention Packet (DIP) under global budget, that classified patients based on the combination of principal diagnosis and procedures. Our study explores the impact of DIP payment reform on hospital selection of patients undergoing total hip/knee arthroplasty (THA/TKA) or with arteriosclerotic heart disease (AHD) from July 2017 to June 2021 in a large city. We used a difference-in-differences approach to compare the changes in patient age, severity reflected by the Charlson Comorbidity Index (CCI), and a measure of treatment intensity [relative weight (RW)] in hospitals that were and were not subject to DIP incentives before and after the DIP payment reform in July 2019. Compared with non-DIP pilot hospitals, trends in patient age after the DIP reform were similar for DIP and non-DIP hospitals for both conditions, while differences in patient severity grew because severity in DIP hospitals increased more for THA/TKA (P = 0.036) or dropped in non-DIP hospitals for AHD (P = 0.011) following DIP reform. Treatment intensity (measured via RWs) for AHD patients in DIP hospitals increased 5.5% (P = 0.015) more than in non-DIP hospitals after payment reform, but treatment intensity trends were similar for THA/TKA patients in DIP and non-DIP hospitals. When the DIP payment reform in China was introduced just prior to the pandemic, hospitals subject to this reform responded by admitting sicker patients and providing more treatment intensity to their AHD patients. Policymakers need to balance between cost containment and the unintended consequences of prospective payment systems, and the DIP payment could also be a new alternative payment system for other countries.