Creating and Implementing a Principal Investigator Tool Kit for Enhancing Accrual to Late Phase Clinical Trials: Development and Usability Study
Bibliographic record
Abstract
BACKGROUND: Accrual to oncology clinical trials remains a challenge, particularly during the COVID-19 pandemic. For late phase clinical trials funded by the National Cancer Institute, the development of these research protocols is a resource-intensive process; however, mechanisms to optimize patient accrual after trial activation are underdeveloped across the National Clinical Trial Network (NCTN). Low patient accrual can lead to the premature closure of clinical trials and can ultimately delay the availability of new, potentially life-saving therapies in oncology. OBJECTIVE: The purpose of this study is to formally create an easily implemented tool kit of resources for investigators of oncology clinical trials within the NCTN, specifically the NRG Oncology cooperative group, in order to optimize patient accrual. METHODS: NRG Oncology sought to formally develop a tool kit of resources to use at specific time points during the lifetime of NRG Oncology clinical trials. The tools are clearly described and involve the facilitation of engagement of the study principal investigator with the scientific and patient advocate community during the planning, activation, and accrual periods. Social media tools are also leveraged to enhance such engagement. The principal investigator (PI) tool kit was created in 2019 and thereafter piloted with the NRG Oncology/Alliance NRG-LU005 phase II or III trial in small-cell lung cancer. The PI tool kit was developed by the NRG Oncology Protocol Operations Management committee and was tested with the NRG/Alliance LU005 randomized trial within the NCTN. RESULTS: NRG Oncology/Alliance NRG-LU005 has seen robust enrollment, currently 127% of the projected accrual. Importantly, many of the tool kit elements are already being used in ongoing NRG Oncology trials, with 56% of active NRG trials using at least one element of the PI tool kit and all in-development trials offered the resource. This underscores the feasibility and potential benefits of deploying the PI tool kit across all NRG Oncology trials moving forward. CONCLUSIONS: While clinical trial accrual can be challenging, the PI tool kit has been shown to augment accrual in a low-cost and easily implementable fashion. It could be widely and consistently deployed across the NCTN to improve accrual in oncology clinical trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT03811002; https://clinicaltrials.gov/ct2/show/NCT03811002.
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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 | 0.084 | 0.233 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".