Facilitating accrual to cancer control and supportive care trials: the clinical research associate perspective
Bibliographic record
Abstract
BACKGROUND: Accrual to Cancer Control and Supportive Care (CCL) studies can be challenging. Our objective was to identify facilitators and perceived barriers to successful Children's Oncology Group (COG) CCL accrual from the clinical research associate (CRA) perspective. METHODS: A survey was developed that focused on the following features from the institutional perspective: (1) Components of successful accrual; (2) Barriers to accrual; (3) Institutional changes that could enhance accrual; and (4) How COG could facilitate accrual. The survey was distributed to the lead CRA at each COG site with at least 2 CCL accruals within the previous year. The written responses were classified into themes and sub-themes. RESULTS: 57 sites in the United States (n = 52) and Canada (n = 5) were contacted; 34 (60%) responded. The four major themes were: (1) Staff presence and dynamics; (2) Logistics including adequate numbers of eligible patients; (3) Interests and priorities; and (4) Resources. Suggestions for improvement began at the study design/conception stage, and included ongoing training/support and increased reimbursement or credit for successful CCL enrollment. CONCLUSIONS: The comments resulted in suggestions to facilitate CCL trials in the future. Soliciting input from key team members in the clinical trials process is important to maximizing accrual rates.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.239 | 0.350 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".