The fate of an unsatisfactory urine cytology test among patients with urothelial carcinoma
Bibliographic record
Abstract
OBJECTIVE: To determine the outcome of patients with a urinary cytology test that is unsatisfactory (UUCyt) for evaluation (<50 urothelial cells) to guide the clinical decision-making process, as currently there are no guidelines to aid in interpreting this result and directing further investigations. PATIENTS AND METHODS: We retrospectively reviewed 142 patients, with 265 instances of UUCyt, in our bladder cancer database and by chart review. The cytology, cystoscopy and pathology results in the subsequent 12 months after a UUCyt result were reviewed, and the incidence of new and recurrent genitourinary tract cancers was calculated. RESULTS: All patients had a previous history of, or developed, urothelial carcinoma during the follow-up. There were 41 instances (16.3%) in which bladder cancer was evident at the time of the UUCyt and 29% of these tumours were high-grade. There were another 44 instances (17.5%) in which new or recurrent bladder cancer developed in the subsequent year after a UUCyt test, and many (38.6%) of these tumours were high-grade. CONCLUSION: The incidence of urothelial carcinoma after a UUCyt was high (33.9%) with a substantial number of high-grade (34%) tumours, implying that a UUCyt result cannot be interpreted as negative for malignancy. Therefore, in these cases, the urologist must depend on cystoscopy to make a diagnosis.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".