The Clinical Applications of Serum and Urinary Biomarkers in Prostate Cancer
Bibliographic record
Abstract
At every stage of the prostate cancer journey from screening and diagnosis to management of advanced disease, patients and clinicians face dilemmas and decisions that can impact long-term outcomes. Although traditional risk stratification in prostate cancer is based on serum prostate specific antigen, clinical stage and Gleason score, in recent years, biomarkers have been developed that may be useful in several clinical scenarios. Biomarkers that can accurately predict an individual patient’s risk, prognosis, and response to specific treatments could lead to improvements in decision-making and clinical care. Although there is evidence to support the use of biomarkers to guide management decisions, the optimal scenario in which to use them, how to interpret the results, and how to incorporate those results into clinical decision-making can be confusing. Nevertheless, in the era of personalized and precision medicine, it is important for clinicians to be aware of what tests are available, what clinical questions they seek to answer, and what limitations they have. This review focuses on the serum and urine biomarkers for the management of prostate cancer that have been under intense investigation in recent years.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".