Institutional variability in the accuracy of urinary cytology for predicting recurrence of transitional cell carcinoma of the bladder
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To assess the contemporary inter-institutional accuracy of urinary cytology in predicting the recurrence of transitional cell carcinoma (TCC) of the bladder, in a large multi-institutional cohort from four continents, as cystoscopy and urinary cytology represent the 'gold standards' for surveillance of TCC recurrences, but the ability of cytology to predict recurrence varies. PATIENTS AND METHODS: Ten institutions contributed 2542 patients with a history of superficial TCC, of whom 898 had TCC recurrence. Age- and gender-adjusted logistic regression models were used to evaluate the association between urine cytology and TCC recurrence. The predictive accuracy derived from the logistic regression model was tested using the area under the receiver operating characteristic curve. The resulting predictive accuracy estimates were internally validated with 200 bootstrap re-samples. RESULTS: The mean (range across institutions) age of the patients was 65 (48-69) years and 75 (67-87)% were men. Cytology was positive in 19 (10-38)% of patients; recurrence was identified in 35 (27-54)% of patients. The sensitivity was 38-65% across institutions. Urinary cytology varied significantly in its ability to predict recurrence of bladder cancer. Institution-specific predictive accuracy adjusted for gender and age was 0.627-0.893. Stratifying by grade and stage only partly attenuated the discrepancies between centres. CONCLUSIONS: The variability of urinary cytology results was very appreciable among the 10 centres and ranged from poor (63%) to excellent (89%).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it