DEFINING LUPUS TRIAL RECRUITMENT CHALLENGES AND IDENTIFYING COLLABORATIVE SOLUTIONS THROUGH THE LUPUS CLINICAL INVESTIGATORS NETWORK (LUCIN)
Bibliographic record
Abstract
PV085 / #710 Poster Topic: AS11 - Epidemiology and Public Health Background/Purpose Lupus Therapeutics (LT), the clinical affiliate of the Lupus Research Alliance, oversees the Lupus Clinical Investigators Network (LuCIN), a network of premier research sites in North America formed to accelerate and improve the conduct of clinical trials for the development of new therapies. Each year, a survey of LuCIN investigators and research teams is conducted to address broad topics, focusing on challenges and solutions to recruiting for lupus clinical trials. Methods The 2024 LuCIN annual survey collected data from January 7 to February 18, 2024. Questions focused on assessing views of challenges and solutions for conducting lupus clinical trials in North America. The survey methodology included the perspectives of investigators, study coordinators and other clinical site staff to broaden the perspectives of responses. Descriptive statistics were used to analyze the survey responses. Results 114 investigators and study coordinators identified industry regulatory or policy actions that could positively improve recruitment of underrepresented populations in lupus clinical trials which include increasing patient compensation to offset costs and burdens of participation (82%), providing additional funding or incentives for engagement efforts (70%), and revising eligibility criteria that may disproportionately exclude historically underrepresented populations (60%) (Table 1). Most respondents affirmed that the inclusion/exclusion criteria are too restrictive (83%), and more than half find it difficult to recruit patients (57%) in the clinical trials they participate in. 70% of respondents also shared the existing or prior use of approved medications for lupus is a common reason for participant exclusion in industry-sponsored clinical trials. Most respondents utilize in-house referrals (92%) and their own lupus clinic registries, biorepositories or databases (73%) to engage or recruit patients into clinical trials (Table 2). Only 11% of respondents say they utilized social media for recruitment or other outreach communications for clinical trials, marking an area of strategic opportunity. According to the Investigators, most of their patients commute over 1 hour to their site (86%). Organizations like Lupus Therapeutics can best support sites to effectively engage and recruit historically underrepresented populations in clinical trials by partnering with community organizations (74%) and providing support in developing culturally sensitive outreach materials (73%). Almost half of respondents believe training to improve recruitment of underserved patients (48%) and disease activity/scale training (46%) would benefit their site staff teams. Investigators proposed solutions to recruitment challenges which included maintaining sufficient staff, providing additional stipends to support transportation, parking, food, hotel and childcare, as well as enhancing community-based interventions enhancing referral networks through provider collaboration, database filters, educating colleagues and creating incentives for physicians to refer patients. Table 1 Table 2 Conclusions Survey findings highlight the need for tailored strategies to improve lupus trial recruitment, particularly among underrepresented populations. Perspectives provided by study teams across an omnipresent lupus clinical trials network in North America underscore the need to address challenges related to clinical trial conduct including revising eligibility criteria, increasing participant compensation and enabling better support for site teams to engage and recruit more patients. Future directions include a focus on understanding specific eligibility criteria that mitigate recruitment difficulties and implement new strategies in addition to social media outreach for clinical trials. This work highlights tangible opportunities in lupus research to promote equity in clinical trials and design future studies that enable easier recruitment to advance therapeutic options for lupus patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.211 | 0.254 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".