Molecular biomarkers in prostate cancer
Bibliographic record
Abstract
BACKGROUND: After more than two decades of clinical use, serum prostate-specific antigen (PSA) has increased the early diagnosis of prostate cancer, detecting the disease even when small volumes are present. Although stage migration of prostate cancer has occurred, PSA has well-known limitations, despite attempts at refinement and modification, such as the use of PSA velocity, which have been used to improve it. New biomarkers for prostate cancer have been discovered, with promising early results. OBJECTIVE/METHODS: This article reviews the ubiquitous current literature on biomarkers in prostate cancer. A search using MEDLINE and EMBASE databases was performed and those articles reporting biomarkers in prostate cancer with clinically significant findings in terms of detection were analyzed. Immunohistochemical markers were not considered for this review. RESULTS/CONCLUSION: Despite many markers being promising, no single marker has satisfied the criteria as a perfect candidate. Limited clinical use of IL-6, TGF-β1 and PCA3 has commenced, and further widespread availability of these tests is expected in the coming years. The future lies in artificial neural networks and panels of markers instead of individual assays. Although PSA has some well-known limitations, it is at present the best marker available for prostate cancer when used in conjunction with nomograms or risk calculators.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.006 | 0.003 |
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".