Prostate cancer circulating tumor cells: automated and manual enumeration after isolation via size-based filtration of pre-treatment patient samples.
Bibliographic record
Abstract
CTCs have emerged as a potential source of clinical significance. But with numerous isolating systems currently available, the numbers of captured CTCs vary widely. At this point, CellSearch remains the only FDA-approved system with clinical significance whereby the results could be used to monitor patients with metastatic colon, breast, or prostate cancer. However, its inability to isolate CTCs from non-high risk prostate cancer patients or CTCs that are EpCAM-negative has led to criticism. In this study, we have shown that size-based filtration successfully isolates CTCs from patients with localized and metastatic prostate cancer. We have also shown that CTCs can be successfully isolated from low and intermediate risk groups. Additionally, clusters of CTCs were preserved and isolated in all localized risk groups and metastatic patients. Furthermore, we enumerated the isolated CTCs using automated and manual methods in low risk, intermediate risk, high risk, and metastatic prostate cancer. The automated and manual counts were comparable. Moreover, the amounts of clusters and the size of clusters correlated with the status and stage of prostate cancer.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".