Learned lessons from a US-wide outreach program to broaden enrollment to the PROMISE Registry, a prostate cancer genetic registry.
Notice bibliographique
Résumé
10628 Background: Updates to NCCN genetic testing recommendations and approved PARPi treatments for prostate cancer (PCa) patients (pts) have clarified the need for genetic registries to identify pts for novel treatments and understand real-world effects of targeted therapies. PROMISE (NCT04995198) is a US prospective genetic registry that has deployed an outreach program to broaden enrollment beyond the usual approach of academic medical centers as recruitment sites. PROMISE aims to create a PCa genetic registry by enrolling and screening 5,000 PCa pts via germline testing to identify 500 for long-term follow-up with germline mutations in genes of interest. Methods: The outreach program was initiated in May 2021 alongside enrollment. The program aims to supplement ongoing recruitment at 23 institutions by broadening enrollment to include populations and areas not served by academic medical centers. Direct-to-pt outreach was prioritized via partnerships with PCa advocacy organizations with groups and geographic areas with high prevalence of PCa. Online activities include webinars, interviews, podcasts, articles, partner email blasts, and newsletters. In-person activities include tabling and presentation at patient- and provider-facing conferences, and tabling at pt walks. Letters were sent introducing PROMISE through the Maryland Cancer Registry to individuals with PCa. A dedicated team including marketing, partnerships and engagement, and website SEO specialists support the program. Funding for the outreach program is provided by the study funder, Advancing Clinical Trials (ACT). Results: As of January 2023, study accrual is 54% ahead of initial projections. 2,178 have been enrolled and 219 are eligible for long-term follow-up across 48 states, with most enrollment occurring on the east and west coasts. Race/ethnicity distribution is as follows: American Indian or Alaska Native 0.4%, Asian 2.0%, Black 3.9%, Hispanic 1.8%, Native Hawaiian or Pacific Islander 0.1%, White 76.4%, unknown 0.4%, and no response provided 16.3%. Conclusions: Traditional recruitment efforts by academic medical centers, when supplemented with direct-to-pt outreach yields increased enrollment. Effective components include 1) partnerships with PCa advocacy organizations, 2) communication from PIs, Investigators, and other medical professionals via webinars and interviews with clinically relevant topics and Q&A, and 3) varied methods of outreach. While the program has led to high accrual, distribution of enrolled participants supports findings from other genetic and genomic registries indicating that increasing diversity continues to be a challenge. Moving forward, we will continue to work with outreach partners to find well-targeted, efficient ways to reach PCa patients with attention to increasing participant diversity. Clinical trial information: NCT04995198 .
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 | 0,047 | 0,070 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,006 | 0,009 |
| Science ouverte | 0,006 | 0,011 |
| Intégrité de la recherche | 0,006 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,005 |
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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».