An up-to-date catalogue of urinary markers for the management of prostate cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: Prostate cancer (PCa) is the most commonly diagnosed cancer in men. Poor specificity and sensitivity of total PSA often results in over and sometimes underdetection/treatment. Therefore, more specific and sensitive biomarkers for the detection and monitoring especially of clinically significant PCa as well as treatment-specific markers are much sought after. In this field, urine has emerged as a promising noninvasive source of biomarkers. RECENT FINDINGS: RNA-based biomarkers are the most extensively studied type of urinary nucleic acids. ERG-Score/MiPS (Mi-Prostate Score) and SelectMDx might be considered as additional parameters together with clinical and imaging modalities to decrease unnecessary biopsies. miR Sentinel Tests could make it possible to accurately detect the presence of cancer and to distinguish low-grade from high-grade disease. In men with previous negative biopsies, PCA3 may suggest the need to repeat biopsy. SUMMARY: The definitive role of these markers and their clinical benefit needs future validation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".