The detection of genetic markers of bladder cancer in urine and serum
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review the most recent publications focusing on the use of genetic markers (DNA, RNA and nucleosides) in urine and serum and provide an opinion on their potential utility for screening, diagnosis and prognosis of urothelial carcinoma. RECENT FINDINGS: Several studies have shown the diagnostic utility of urine tests based on improved microsatellite analysis of loss-of-heterozygosity, detection of fibroblast growth factor receptor 3 mutations, detection of single RNA and multiple gene signatures as well as nucleoside profiles. Although of interest, all these studies lack appropriate controls and validation before being considered as serious candidate clinical biomarkers. The presence of fibroblast growth factor receptor 3 mutations in tumors was identified as a hallmark of tumors of low malignant potential. Serum DNA analysis of hypermethylation of a set of genes shows promise as an indicator of cancer progression and mortality. Finally, a case-control study of 775 patients and 397 controls showed that DNA hypomethylation of blood cells combined with smoking habits can provide stratification of cancer risk that may be very helpful in conceiving bladder cancer screening studies. SUMMARY: The challenge is not so much to identify better markers or methods but rather to commit to implementing them into clinical practice by stimulating and funding the clinical trials required.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".