Assembling a network to promote translational bladder cancer research in Canada
Bibliographic record
Abstract
Bladder cancer research has historically lagged behind efforts in other disease sites with substantial underfunding relative to the heavy morbidity and mortality suffered by patients. Alongside increasing advocacy however, more recent advances in our understanding of the molecular biology of bladder cancer has ushered in a period of renaissance with exciting prospects for novel, precise diagnostics and therapeutics. Given significant and diverse assets within the research community across Canada, an inaugural translational research forum was convened to identify research gaps and strengths, and to formalize investigational themes that would be apposite for multi-institutional collaboration. The virtual meeting brought together a multi-disciplinary network of genitourinary cancer researchers, including clinicians and basic scientists, and entailed detailed environmental scans of the Canadian clinical and translational research landscape as well as selected “elevator pitches” of potential research themes. The results of these discussions are detailed herein and have provided the impetus to formalize the Canadian Bladder Cancer Research Network (CBCRN). Working groups have been created to focus future multi-institutional collaborations in four inter-related initiatives: biomarker development, epigenetic targeting, immuno-oncology and the microbiome.
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 | 0.039 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.025 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".