The Importance of Being Grade 3: A Plea for a Three-tier Hybrid Classification System for Grade in Primary Non–muscle-invasive Bladder Cancer
Bibliographic record
Abstract
Grade is an important determinant of progression in non-muscle-invasive bladder cancer. Although the World Health Organization (WHO) 2004/2016 grading system is recommended, other systems such as WHO1973 and WHO1999 are still widely used. Recently, a hybrid (three-tier) system was proposed, separating WHO2004/2016 high grade (HG) into HG/grade 2 (G2) and HG/G3 while maintaining low grade. We assessed the prognostic performance of HG/G3 and HG/G2. Three independent cohorts with 9712 primary (first diagnosis) Ta-T1 bladder tumors were analyzed. Time to progression was analyzed with cumulative incidence functions and Cox regression models. Harrell's C-index was used to assess discrimination. Time to progression was significantly shorter for HG/G3 than for HG/G2 in multivariable analyses (cohort 1: hazard ratio [HR] = 1.92; cohort 2: HR = 2.51, and cohort 3: HR = 1.69). Corresponding progression risks at 5 yr were 18%, 20%, and 18% for HG/G3 versus 7.3%, 7.5%, and 9.3% for HG/G2, respectively. Cox models using hybrid grade performed better than models with WHO2004/2016 (all cohorts; p < 0.001). For the three cohorts, C-indices for WHO2004/2016 were 0.69, 0.62, and 0.75, while, for hybrid grade, C-indices were 0.74, 0.68, and 0.78, respectively. Subdividing the HG category into HG/G2 and HG/G3 stratifies time to progression and supports the recommendation to adopt the hybrid grading system for Ta/T1 bladder cancers.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".