Learned lessons from a US-wide outreach program to broaden enrollment to the PROMISE Registry, a prostate cancer genetic registry.
Bibliographic record
Abstract
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 .
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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 | 0.047 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".