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.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".