International Society of Urological Pathology (ISUP) Consensus Conference on Current Issues in Bladder Cancer
Bibliographic record
Abstract
Emerging data on T1 bladder cancer subcategorization (aka substaging) suggests a correlation with oncological outcomes. The International Society of Urological Pathology (ISUP) organized the 2022 consensus conference in Basel, Switzerland to focus on current issues in bladder cancer and tasked working group 3 to make recommendations for T1 subcategorization in transurethral bladder resections. For this purpose, the ISUP developed and circulated a survey to their membership querying approaches to T1 bladder cancer subcategorization. In particular, clinical relevance, pathological reporting, and endorsement of T1 subcategorization in the daily practice of pathology were surveyed. Of the respondents of the premeeting survey, about 40% do not routinely report T1 subcategory. We reviewed literature on bladder T1 subcategorization, and screened selected articles for clinical performance and practicality of T1 subcategorization methods. Published literature offered evidence of the clinical rationale for T1 subcategorization and at the conference consensus (83% of conference attendants) was obtained to report routinely T1 subcategorization of transurethral resections. Semiquantitative T1 subcategorization was favored (37%) over histoanatomic methods (4%). This is in line with literature findings on practicality and prognostic impact, that is, a shift of publications from histoanatomic to semiquantitative methods or by reports incorporating both methodologies is apparent over the last decade. However, 59% of participants had no preference for either methodology. They would add a comment in the report briefly stating applied method, interpretation criteria (including cutoff), and potential limitations. When queried on the terminology of T1 subcategorization, 34% and 20% of participants were in favor of T1 (microinvasive) versus T1 (extensive) or T1 (focal) versus T1 (nonfocal), respectively.
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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 | 0.107 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".