Corrigendum to “Proposal for a Novel Histological Scoring System as a Potential Grading Approach for Muscle-invasive Urothelial Bladder Cancer Correlating with Disease Aggressiveness and Patient Outcomes” [European Urology Oncology (2023)]
Bibliographic record
Abstract
The authors regret that the author ‘Maurizio Colecchia’ was not included in the author list. This has now been corrected as above. The authors would like to apologise for any inconvenience caused. Proposal for a Novel Histological Scoring System as a Potential Grading Approach for Muscle-invasive Urothelial Bladder Cancer Correlating with Disease Aggressiveness and Patient OutcomesEuropean Urology OncologyPreviewTake Home Message Grading of muscle-invasive bladder cancer according to the current World Health Organization criteria is controversial due to its limited prognostic value. We propose a specific, prognostically relevant scoring system for muscle-invasive bladder cancer integrating histomorphological phenotype, stromal tumor infiltrating lymphocytes, tumor budding, and growth and spreading patterns. Full-Text PDF Open Access
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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 | 0.003 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.043 | 0.042 |
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".