Clinical Utility of Bladder Cancer Biomarkers
Bibliographic record
Abstract
Each year, there are an estimated 550 000 diagnoses of bladder cancer worldwide, and almost 200 000 deaths from bladder cancer. The need for frequent follow-up, including invasive procedures like cystoscopy, repetitive procedures like transurethral resection of bladder tumors and intravesical instillation therapy in non-muscle invasive stages, as well as systemic treatment with or without radical local treatment in advanced stages, makes bladder cancer one of the most expensive cancers to treat. Prognostic and predictive biomarkers have the potential to fundamentally change bladder cancer treatment algorithms, which may result in improved patient comfort and oncological outcomes and may also decrease the socioeconomic burden of the disease. Intense research has resulted in the recent approval by the U. S. Food and Drug Administration of the first agent for this disease that targets a specific mutation (fibroblast-growth factor receptor). Yet, many areas of bladder cancer diagnosis and treatment have remained unchanged for decades, and this is only in part due to their therapeutic success. In order to integrate biomarkers into clinical practice patterns, specific considerations for the different disease stages and settings should be kept in mind. Especially in the setting of screening, work-up of hematuria, as well as surveillance of patients with non-muscle invasive bladder cancer, (urine-)biomarkers may prove useful. They must, however, demonstrate a high enough sensitivity to pick up a cancer diagnosis or recurrence, allow easy handling (preferably a point-of-care setting) and adequate cost–benefit relationships, while also providing additional information to a full work-up. A biomarker to identify patients with muscle invasive bladder cancer who are in need of—and likely to respond to—neoadjuvant therapy would be very useful. In later disease, early detection of recurrence or progression, as well as biomarkers guiding treatment decisions between the available systemic agents, will be paramount for improved patient care.
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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) | 0.007 | 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".