Bibliographic record
Abstract
PURPOSE OF REVIEW: Bladder cancer is a common disease whose natural history can be unpredictable. As such, there is an urgent clinical need to identify biomarkers that will improve the care of patients by allowing a more individualized approach. Noncoding RNAs (ncRNAs) are a recently identified subgroup of RNAs whose mature species are not translated into proteins. Here, we review knowledge of ncRNA in bladder cancer, with a focus upon their role in high-risk nonmuscle invasive tumors. RECENT FINDINGS: There have been a number of articles reporting the ability of microRNAs to help evaluate patients with nonmuscle invasive bladder cancer. New long ncRNA species have been evaluated for the first time in bladder cancer. Competing endogenous RNAs and enhancer RNAs show interesting functional and regulatory effects in other cancers, but have yet to be evaluated in bladder cancer. SUMMARY: Novel RNA species are increasingly being used to help prognosticate patients with bladder cancer and to understand key oncological events in the evolution of this disease. Future work is needed to validate potential clinical utility of the RNA species described.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".