Abstract IA012: The NCTN biospecimen banks resource
Bibliographic record
Abstract
Abstract The National Clinical Trials Network (NCTN) Biospecimen Banks receive, process, store, and distribute various types of human biospecimens collected on NCTN clinical trials. The NCTN Biospecimen Banks serve as a critical resource for the cancer research community by providing researchers with quality, clinically annotated biospecimens, ultimately supporting a variety of translational research efforts. Biospecimen access is open to qualified investigators across the entire research community, not just NCTN members, providing opportunities for secondary use in hypothesis-driven studies. Transparent access to these resources is facilitated through the NCTN Catalog and NCTN Navigator, which include publicly available biospecimen inventories. Investigators can explore available biospecimens and submit requests that are subsequently reviewed for scientific merit by the National Cancer Institute (NCI). The NCTN Biospecimen Banks are a unique resource that enables the use of diverse, clinically annotated biospecimens to support research addressing cancer health disparities, precision oncology, and other translational research efforts that will ultimately improve cancer prevention, treatment, and outcomes. Citation Format: Heather A. Lankes, Mark A. Watson, Jeffry P. Simko, Nilsa C. Ramirez, Scott D. Jewell, Lois E. Shepherd, Sumana Dey, Hala R. Makhlouf. The NCTN biospecimen banks resource [abstract]. In: Proceedings of the 18th AACR Conference on the Science of Cancer Health Disparities; 2025 Sep 18-21; Baltimore, MD. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2025;34(9 Suppl):Abstract nr IA012.
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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 | 0.020 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.425 | 0.302 |
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".