ReCAP: Clinical Trial Assessment of Infrastructure Matrix Tool to Improve the Quality of Research Conduct in the Community
Bibliographic record
Abstract
QUESTION ASKED: Is there a tool for sites engaged in cancer clinical research to use to assess their infrastructure and improve their research conduct toward exemplary levels of performance beyond the standard of Good Clinical Practice (GCP)? SUMMARY ANSWER: The NCI Community Cancer Center Program (NCCCP) sites, with NCI Clinical Trial advisor input, created a “Clinical Trials Best Practice Matrix” self-assessment tool to assess research infrastructure. The tool identified nine attributes (eg, physician engagement in clinical trials, accrual activity, clinical trial portfolio diversity), each with three progressive levels (I – III) for sites to score infrastructural elements from less (I) to more (III) exemplary. For example, a level-one site might have active Phase III treatment trials in two to three disease sites and review their portfolio diversity once a year, whereas a level-three site has active Phase II and also Phase I or I/II trials across five or more disease sites and reviews their portfolio quarterly. The tool also provided a road map toward more exemplary practices. METHODS: From 2011 to 2013, 21 NCCCP sites self-assessed their programs with the tool annually. Sites reported significant increases in level III (more exemplary) scores across the original nine attributes combined (P < .001 [see Figure 1 ]). During 2013 to 2014, NCI collaborators conducted a five-step formative evaluation of the tool resulting in expansion of attributes from nine to 11 and a new name: the Clinical Trials Assessment of Infrastructure Matrix, or CT AIM, tool which is described and fully presented in the manuscript. BIAS, CONFOUNDING FACTOR(S), DRAWBACKS: Tool scores are self-reported which are subject to potential bias. The tool was developed by community hospital based cancer centers and has not been psychometrically validated. Use of scores for ranking between programs is not recommended at this time. The attributes and indicators in the tool may need to be adapted for other settings (eg, academic or private practice settings), and over time as research practice evolves. Not all sites can, or want to, move beyond the provision of GCP in their research programs. Adherence to GCPs meets the minimum criteria for clinical trial conduct and some of the attributes in the CT AIM can be both fiscally and administratively challenging to implement. REAL-LIFE IMPLICATIONS: The CT AIM tool gives community programs a tool to assess their research infrastructure as they strive to move beyond the basics of GCP to more exemplary performance. Experience within the NCCCP program suggests the CT AIM tool may be useful for improving programmatic quality, benchmarking research performance, reporting progress, and communicating program needs with institutional leaders. The tool may also be a companion to existing clinical trial education and program resources. Although used in a small group of community cancer centers, the tool may be adapted as a model in other disease disciplines. [Figure: see text]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.342 | 0.598 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.014 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".