Urinary-Based Markers for Bladder Cancer Detection
Bibliographic record
Abstract
Background The use of urine markers for diagnosis and surveillance has been a topic of broad interest and ongoing controversies in the management of patients with bladder cancer. There has been a constant quest for markers that demonstrate clinical utility. Aim In the framework of the International Consultation on Urological Diseases 2019 on Molecular Biomarkers in Urologic Oncology, a comprehensive review of literature on urinary biomarkers for bladder cancer has been performed. Results Currently available urinary markers include protein-based markers, RNA-based markers, and DNA-based markers. The introduction of high-throughput analysis technologies provides the opportunity to assess multiple parameters within a short period of time, which is of interest for RNA-based, DNA-based, and protein-based marker systems. A comprehensive analysis of molecular alterations in urine samples of bladder cancer patients may be of interest not only for diagnosis and surveillance but also for non-invasive longitudinal assessment of molecular, potentially therapy-relevant, alterations. However, most systems lack prospective validation within well-designed trials and have not been broadly implemented in daily clinical practice. Conclusions Because of limited data from prospective trials, the routine use of any urine marker except cytology is not considered as standard of care in international guidelines. There is an urgent need for prospective trials of urine markers to answer specific clinical questions.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".