Consultation on UTUC II Stockholm 2022: diagnostic and prognostic methods—what’s around the corner?
Bibliographic record
Abstract
PURPOSE: To map current literature and provide an overview of upcoming future diagnostic and prognostic methods for upper tract urothelial carcinoma (UTUC), including translational medical science. METHODS: A scoping review approach was applied to search the literature. Based on the published literature, and the experts own experience and opinions consensus was reached through discussions at the meeting Consultation on UTUC II in Stockholm, September 2022. RESULTS: The gene mutational profile of UTUC correlates with stage, grade, prognosis, and response to different therapeutic strategies. Analysis of pathway proteins downstream of known pathogenic mutations might be an alternative approach. Liquid biopsies of cell-free DNA may detect UTUC with a higher sensitivity and specificity than urinary cytology. Extracellular vesicles from tumour cells can be detected in urine and may be used to identify the location of the urothelial carcinoma in the urinary tract. 3D microscopy of UTUC samples may add information in the analysis of tumour stage. Chemokines and chemokine receptors were linked to overall survival and responsiveness to neoadjuvant chemotherapy in muscle-invasive bladder cancer, which is potentially also of interest in UTUC. CONCLUSION: Current diagnostic methods for UTUC have shortcomings, especially concerning prognostication, which is important for personalized treatment decisions. There are several upcoming methods that may be of interest for UTUC. Most have been studied for urothelial carcinoma of the bladder, and it is important to keep in mind that UTUC is a different entity and not all methods are adaptable or applicable to UTUC.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".