Abstract LB148: Perspectives of clinical trial staff on recruitment barriers to a multicenter randomized controlled colorectal polyp prevention trial
Bibliographic record
Abstract
Abstract Background: Participant recruitment remains a pervasive issue in clinical trial management. Colorectal cancer (CRC) is a growing cause of cancer-related morbidity and mortality globally, making CRC prevention crucial. COLO-PREVENT is a 10-year multicenter randomized controlled trial platform assessing the efficacy of aspirin versus aspirin plus metformin in a phase 3 study, alongside a phase 2 study of resveratrol compared to placebo for the prevention of colorectal polyps in high-risk individuals identified via the Bowel Cancer Screening Program (BCSP) in the United Kingdom. It aims to recruit 1300 participants. While the extended trial duration may affect recruitment, additional barriers within a prevention trial population have not been studied. Methods: A questionnaire was distributed to clinical trial staff (research nurses, site coordinators) across 14 sites. The questionnaire focused on recruitment processes, the recruitment barriers noted, as well as strategies to overcome these barriers. Data was thematically analyzed by 2 researchers (FF, MS). Common themes were identified regarding recruitment barriers, as well as strategies to address recruitment barriers. Recruitment strategies were correlated with site randomization numbers to assess effectiveness. Results: There were four key common themes identified by site research teams as participant barriers to recruitment: Logistical requirements of the trial (36.8%), medication concerns (36.8%), ambiguity in the study processes (15.8%), and unclear benefit to participation (10.5%). Sites with higher randomization numbers worked closely with their local BCSP teams to ensuring they had accurate lists of potential participants. Subsequently, multiple follow-up strategies using various mediums (e.g. phone calls, emails, and in-person contact) were felt to be beneficial. Additionally, blood tests required for screening prior to recruitment were used for health monitoring and reassurance regarding medication side effects leading to a more personalized experience for participants. Conclusion: This study highlights key reasons why participants may decline to join prevention trials, including logistical difficulties, medication concerns, and unclear perceived benefits. Additional factors requiring further exploration include: the inclusion of a symptomatically healthy population which is a known recruitment barrier. Strategies such as strong integration with the local BCSP team, personalized participant engagement, and participant reassurance with blood tests may improve recruitment outcomes in CRC prevention trials. Future research should focus on understanding participant perspectives first-hand, examining other cancer prevention disease areas, and developing targeted solutions to ensure more effective prevention clinical trial design. Citation Format: Frankie Fan, Moiz Shakeel, Nafisa Boota, Hilary Adamson, Ajay Verma, Karen Brown. Perspectives of clinical trial staff on recruitment barriers to a multicenter randomized controlled colorectal polyp prevention trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_2):Abstract nr LB148.
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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 | 0.609 | 0.732 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".