Clinical Trial Metrics: The Complexity of Conducting Clinical Trials in North American Cancer Centers
Bibliographic record
Abstract
PURPOSE: Cancer clinical trials offices (CTOs) support the investigation of cancer prevention, early detection, and treatment at cancer centers across North America. CTOs are a centralized resource for clinical trial conduct and typically use research staff with expertise in four functional areas of clinical research: finance, regulatory, clinical, and data operations. To our knowledge, there are no publicly available benchmark data sets that characterize the size, cost, volume, and efficiency of these offices, nor whether the metrics differ by National Cancer Institute (NCI) designation. The Association of American Cancer Institutes (AACI) Clinical Research Innovation (CRI) steering committee developed a survey to address this knowledge gap. METHODS: An 11-question survey that addressed CTO budget, accrual and trial volume, full-time equivalents (FTEs), staff turnover, and activation timelines was developed by the AACI CRI steering committee and sent to 92 academic cancer research centers in North America (n = 90 in the United States; n = 2 in Canada), with 79 respondents completing the survey (86% completion rate). RESULTS: The number of FTE employees working in the CTOs ranged from 4.5 to 811 (median, 104). The median number of analytic cases (ie, newly diagnosed or received first course of treatment) reported by the main center was 3,856. Annual CTO budgets ranged from $250,000 to $23,900,000 (median, $8.2 million). The median trial activation time, based on 61 centers, was 167 days. The median number of accruals per center was 480 (range, 5-6,271) and median number of trials per center was 282 (range, 31-1,833). Budget and FTE ranges varied by NCI designation. CONCLUSION: The response rate to the survey was high. These data will allow cancer centers to evaluate their CTO infrastructure, funding, portfolio, and/or accrual goals as compared with peers. A wide range in each of the outcomes was noted, in keeping with the wide variation in size and scope of cancer center CTOs across the United States and Canada. These variations may warrant additional investigation.
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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 | 0.292 | 0.558 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".