Expansion of an Academic Molecular Tumor Board to Enhance Access to Biomarker-Driven Trials and Therapies in the Rural Southeastern United States
Bibliographic record
Abstract
Targeting tumor-specific molecular alterations has shown significant clinical benefit. Molecular tumor boards (MTBs) connect cancer patients with personalized treatments and clinical trials. However, rural cancer centers often have limited access to MTB expertise. We established an academic-community partnership expanding our academic MTB to affiliated rural community cancer centers. We developed a centralized molecular registry of tumors (MRT) to aggregate the comprehensive genomic profiling (CGP) results and facilitate multidisciplinary MTB review. Of the 151 patients included, 87 (58%) had actionable genomic biomarkers, 42 (28%) were eligible for a targeted off-label therapy, and 27 (18%) were matched to a clinical trial. Of those with a clinical trial match, only 1 of 27 (3%) was enrolled in the identified trial. One year into implementation, community oncology providers were anonymously surveyed on persistent barriers to precision treatment utilization. The primary barriers to clinical trial enrollment were the distance to the trial center (70%), lack of transportation (55%), and lack of local trials (50%). This study offers a framework to improve access to molecular expertise, but significant barriers to the equitable use of CGP and trial enrollment persist.
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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 | 0.027 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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, 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".