Impact of Implementing the Paris System for Reporting Urine Cytology in the Performance of Urine Cytology
Bibliographic record
Abstract
OBJECTIVES: We assessed the performance of urine cytology using the Paris System for Reporting Urine Cytology (PSRUC) in comparison to our current system. METHODS: In total, 124 specimens with histologic correlation were reviewed and assigned to the PSRUC categories: benign, atypical urothelial cells (AUCs), suspicious for high-grade urothelial carcinoma (SHGUC), and high-grade urothelial carcinoma (HGUC). Original cytological diagnoses were recorded. RESULTS: Fewer cases were given an AUC diagnosis using the PSRUC in comparison to the original diagnoses (26% vs 39%), while the association of AUCs with subsequent HGUC increased from 33% to 53% with the PSRUC. Using the PSRUC resulted in a higher number of low-grade carcinomas assigned to the benign (40%) rather than the AUC (22%) category. The performance of SHGUC/HGUC diagnoses was similar in both systems (predictive value = 94%). CONCLUSIONS: The PSRUC seems to improve the performance of urine cytology by limiting the AUC category to cases that are more strongly associated with HGUC.
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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 | 0.052 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".