Predicting high risk disease using serum and DNA biomarkers
Bibliographic record
Abstract
PURPOSE OF REVIEW: To explore several serum and genetic-based biomarkers that may prove useful in following men being managed with active surveillance for localized prostate cancer by predicting those that either have the potential to develop, or already harbor occult high grade disease. RECENT FINDINGS: There is increasing evidence that serum biomarkers human Kallikrein 2, early prostate cancer antigen, urokinase-type plasminogen activator/urokinase-type plasminogen activator receptor, transforming growth factor-β1 and interleukin-6/interleukin-6 receptor and genetic biomarkers BRCA1 and BRCA2, Phosphatase and tensin homolog, cellular myelocytomatosis oncogene and NKX3.1 may predict for aggressive high grade disease and are identifiable early in prostate carcinogenesis. SUMMARY: One of the barriers of widespread adoption of active surveillance for low risk, localized prostate cancer is the concern that some patients may harbor occult high-risk disease at diagnosis, or develop more aggressive/noncurable disease not detected by our current well established prognostic factors. This review examines several serum and genetic-based biomarkers that appear to be of value in localized prostate cancer, unlike the vast majority of more established prostate cancer biomarkers that have been validated in far more advanced disease. Although the biomarkers discussed show exciting promise, their clinical utility is unknown, and their role in the active surveillance scenario needs further study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".