Abstract ED01-02: Employing a community partnership approach to recruit African American men to a cancer prevention trial.
Bibliographic record
Abstract
Abstract Background: Many large chemoprevention studies have not recruited significant numbers of minorities. The Selenium and Vitamin E Cancer Prevention Trial (SELECT), a large phase III study, evaluated the impact of selenium and vitamin E on the clinical incidence of prostate cancer. Over 400 SELECT study sites in the United States, Canada, and Puerto Rico recruited men to this trial. The SELECT recruitment goal was 24% minorities, with 20% blacks, 3% Hispanics, and 1% Asians. The goal for blacks was set at 20% because of their proportion in the United States population and their prevalence of prostate cancer. Methods: The minority recruitment strategies in SELECT were to: (1) consider minority recruitment during site selection, (2) expand the eligibility criteria by lowering the age criterion for black men and including men with controlled co-morbid illnesses, (3) develop a national infrastructure, (4) give additional funds to sites with the potential to increase black enrollment, and (5) provide resources to maximize free media opportunities to promote SELECT. Results: SELECT recruitment was intended to last five years but concluded two years ahead of schedule. Of the 35,534 participants enrolled, 21% were minorities with 15% blacks, 5% Hispanics, and 1% Asians. Conclusions: Careful planning, recruitment of large numbers of clinical centers and adequate resources accomplished by the combined efforts of the National Cancer Institute (NCI), Southwest Oncology Group (SWOG), SELECT Recruitment and Adherence Committee (RAC), SELECT Minority and Medically Underserved Subcommittee (MMUS), and the local SELECT sites resulted in attainment of the estimated sample size ahead of schedule and recruitment of the largest percentage of black participants ever randomized to a cancer prevention trial. Citation Format: Elise D. Cook. Employing a community partnership approach to recruit African American men to a cancer prevention trial. [abstract]. In: Proceedings of the Fifth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2012 Oct 27-30; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2012;21(10 Suppl):Abstract nr ED01-02.
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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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".