DEFINING LUPUS TRIAL RECRUITMENT CHALLENGES AND IDENTIFYING COLLABORATIVE SOLUTIONS THROUGH THE LUPUS CLINICAL INVESTIGATORS NETWORK (LUCIN)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,211 | 0,254 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,009 | 0,004 |
| Communication savante | 0,014 | 0,011 |
| Science ouverte | 0,003 | 0,020 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».