Challenges of Recruitment of Breast Cancer Survivors to a Randomized Clinical Trial for Osteoporosis Prevention
Bibliographic record
Abstract
Recruitment of participants was a challenging issue for a statewide, 4-site, randomized, longitudinal trial for osteoporosis prevention. The accrual goal was 273 healthy breast cancer survivors. This federally funded study included a home-based followed by a fitness center-based 24-month intervention with follow-up at 36 months. In this report, recruitment planning, monitoring, and modifications are described, and the cost per enrolled participant is identified. Monthly monitoring of accrual numbers per recruitment strategy at each of 4 catchment areas allowed for early identification of necessary changes in recruitment strategies. Modifications were necessary when only 39% of the overall accrual goal had been attained at the 66% time point into the 18-month recruitment phase. Successful recruitment strategies were intensified, and new strategies were implemented, addressing motivators and deterrents for participation in clinical trials. Because approximately 81% of women were demonstrating bone loss via free dual energy x-ray absorptiometry screening, prevalence of the bone loss problem in survivors was incorporated into the recruitment information. Of 708 women screened via telephone and laboratory/dual energy x-ray absorptiometry testing, 249 were enrolled with 67% at 2 metropolitan sites and 33% at 2 rural sites. Recruitment media costs were approximately US$35 per enrolled participant. When combined with skeletal and laboratory screening, costs were approximately US$480 per enrolled participant. Tracking recruitment efforts in large clinical trials should be ongoing, site-specific, and cost-effective. Changes incorporated early in the recruitment phase addressed unique aspects of rural versus metropolitan areas and resulted in near achievement of accrual goals.
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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 | 0.548 | 0.583 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".