Recent progress with next‐generation biomarkers in muscle‐invasive bladder cancer
Bibliographic record
Abstract
Muscle-invasive bladder cancer is a heterogeneous disease with different clinical phenotypes. Histomorphological variants, variable mutation rates and aberrant protein expression, along with the recently identified molecular subtypes, have been linked to prognosis and response to therapy. Complete response to chemotherapy and outcome after radical cystectomy are difficult to predict. To date, no validated pathological or clinical test exists to predict response. Advances in high-throughput, next-generation, genomic techniques to study the molecular pathways that govern the disease have led to novel strategies for the identification of such biomarkers relevant to muscle-invasive bladder cancer. Progress has been made not only in tissue-based biomarkers, but also in the liquid biopsy field. Liquid biopsies represent an opportunity to obtain patient samples non-invasively at multiple time-points during their treatment course without the need for biopsy. Especially in the metastatic setting, this will allow monitoring of the molecular evolution of the tumor under treatment, which should inform subsequent therapeutic decisions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".