The room where it happens: addressing diversity, equity, and inclusion in National Clinical Trials Network clinical trial leadership
Bibliographic record
Abstract
Many multicenter randomized clinical trials in oncology are conducted through the National Clinical Trials Network (NCTN), an organization consisting of 5 cooperative groups. These groups are made up of multidisciplinary investigators who work collaboratively to conduct trials that test novel therapies and establish best practice for cancer care. Unfortunately, disparities in clinical trial leadership are evident. To examine the current state of diversity, equity, and inclusion across the NCTN, an independent NCTN Task Force for Diversity in Gastrointestinal Oncology was established in 2021, the efforts of which serve as the platform for this commentary. The task force sought to assess existing data on demographics and policies across NCTN groups. Differences in infrastructure and policies were identified across groups as well as a general lack of data regarding the composition of group membership and leadership. In the context of growing momentum around diversity, equity, and inclusion in cancer research, the National Cancer Institute established the Equity and Inclusion Program, which is working to establish benchmark data regarding diversity of representation within the NCTN groups. Pending these data, additional efforts are recommended to address diversity within the NCTN, including standardizing membership, leadership, and publication processes; ensuring diversity of representation across scientific and steering committees; and providing mentorship and training opportunities for women and individuals from underrepresented groups. Intentional and focused efforts are necessary to ensure diversity in clinical trial leadership and to encourage design of trials that are inclusive and representative of the broad population of patients with cancer in the United States.
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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 | 0.534 | 0.697 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.022 | 0.031 |
| Scholarly communication | 0.036 | 0.033 |
| Open science | 0.010 | 0.034 |
| Research integrity | 0.020 | 0.040 |
| Insufficient payload (model declined to judge) | 0.005 | 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".