Accuracy and risk of malignancy for diagnostic categories in urine cytology at a large tertiary institution
Bibliographic record
Abstract
BACKGROUND: At a high-volume center, it became necessary to provide benchmarks for the accuracy and risk of malignancy per urine cytology diagnostic category. The additive sensitivity for the determination of the residual risk of disease was calculated with the goal of determining the performance of cytology and optimal triage, including the number of urine samples, before the detection of malignancy in surveillance patients. METHODS: A 2-year laboratory information system-based search was conducted, and it yielded 587 subjects (695 biopsy and cytology pairs) with histological follow-up. The sensitivity and specificity of cytology for urothelial malignancy, the risk of malignancy per diagnostic category, the additive sensitivity, and the time for conversion from a negative initial cytology result to a positive cytology result were examined. RESULTS: The overall average sensitivity and specificity of cytology were 48.9% and 83.0%, respectively. The additive sensitivity increased with each subsequent cytology and peaked with the third cytology. A median conversion time of 22.2 months from a negative initial cytology result to a positive cytology result and a decline in predictive positive cytology after the fourth cytology were noted. Subcategorization of the atypical category failed to show statistical significance in predicting outcomes of biopsy. Surveillance subjects, as compared to primary subjects, showed a higher sensitivity for the detection of high and low grade cancers. CONCLUSIONS: The findings suggest that atypia favoring malignancy is being appropriately flagged. However, further definition of the atypical category is needed to increase specificity with a better qualitative or quantitative morphological algorithm. This study provides a risk of malignancy for each category for benchmarking and clinical triage. The data suggest that follow-up should include at least 4 consecutive urine specimens over a period of 22.2 months.
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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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".