Beyond the Capture of Circulating Tumor Cells: Next‐Generation Devices and Materials
Bibliographic record
Abstract
Over the last decade, significant progress has been made towards the development of approaches that enable the capture of rare circulating tumor cells (CTCs) from the blood of cancer patients, a critical capability for noninvasive tumor profiling. These advances have leveraged new insights in materials chemistry and microfluidics and allowed the capture and enumeration of CTCs with unprecedented sensitivity. However, it has become increasingly clear that simply capturing and counting tumor cells launched into the bloodstream may not provide the information needed to advance our understanding of the biology of these rare cells, or to allow us to better exploit them in medicine. A variety of advances have now emerged demonstrating that more information can be extracted from CTCs with next-generation devices and materials featuring tailored physical and chemical properties. In this Minireview, the last ten years of work in this area will be discussed, with an emphasis on the groundbreaking work of the last five years, during which the focus has moved beyond the simple capture of CTCs and gravitated towards approaches that enable in-depth analysis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".