NCI workshop on ctDNA in cancer treatment and clinical care
Bibliographic record
Abstract
Detection of cell-free circulating tumor DNA (ctDNA) from solid tumors is a fast-evolving field with significant potential for improving patient treatment outcomes. The spectrum of applications for ctDNA assays is broad and includes very diverse intended uses that will require different strategies to demonstrate utility. On September 14-15, 2023, the National Cancer Institute held an in-person workshop in Rockville, MD titled "ctDNA in Cancer Treatment and Clinical Care." The goal of the workshop was to examine what is currently known and what needs to be determined for various ctDNA liquid biopsy use cases related to treatment and management of patients with solid tumors and to explore how the community can best assess the value of ctDNA assays and technology. Additionally, new approaches were presented that may show promise in the future. The information exchanged in this workshop will provide the community with a better understanding of this field and its potential to affect and benefit decision-making in the treatment of patients with solid tumors.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".