CTCtrap – Circulating Tumor Cells TheRapeutic APheresis: a novel biotechnology enabling personalized therapy for all cancer patients
Bibliographic record
Abstract
Circulating tumor cells (CTC) refer to cells that detach from a primary tumor or metastatic site, circulate in the peripheral blood and may form metastasis. CTC represent a 'liquid biopsy' that can be used to tailor treatment for individual patients. CTC are however rare and can only be obtained for further characterization in a small fraction of patients. In the CTCtrap consortium universities, research institutions and SMEs are linked in a common effort, starting from the simple, but innovative view of using Diagnostic Apheresis (DA), as a way to collect CTC from peripheral blood in cancer patients. A new DA column will be developed to capture CTC and then reintroduce the blood devoid of tumor cells back into the body with the promise to obtain CTC in all patients at risk for recurrence or diagnosed with metastatic disease. The molecular characterization of these CTC is expected to gather new knowledge on metastasis'mechanism, provide a risk assessment and the optimal therapy choice during the course of the disease of cancer patients. The new knowledge on CTC heterogeneity within cancer type and within individuals will allow for the tuning of CTC apheresis to specific cancer types. Prospective pilot studies will be setup to investigate the feasibility of the CTC apheresis in the clinic and their potential diagnostic benefit. Success of CTC apheresis will lead to a radical change in the diagnosis and treatment of 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".