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Learned lessons from a US-wide outreach program to broaden enrollment to the PROMISE Registry, a prostate cancer genetic registry.

2023· article· en· W4379281300 on OpenAlexaff
Channing J. Paller, Justin Lorentz, Leonard J. Appleman, Andrew J. Armstrong, Pedro C. Barata, Robert Dreicer, Jo Ann Elrod, Mark T. Fleming, Christopher George, Elisabeth I. Heath, Maha Hussain, Shifeng Mao, Rana R. McKay, Alicia K. Morgans, Matthew Orton, Роберто Пили, Biren Saraiya, Alexandra Sokolova, Walter M. Stadler, Heather H. Cheng

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsOutreachMedicineFamily medicineProstate cancerCancer registryGenetic testingMedical educationCancerInternal medicinePolitical science

Abstract

fetched live from OpenAlex

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 .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.047
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.251

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.070
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0060.009
Open science0.0060.011
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0190.005

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.

Opus teacher head0.188
GPT teacher head0.450
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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