Stepwise Application of Urine Markers to Detect Tumor Recurrence in Patients Undergoing Surveillance for Non-Muscle-Invasive Bladder Cancer
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Bibliographic record
Abstract
BACKGROUND: The optimal use of urine markers in the surveillance of non-muscle-invasive bladder cancer (NMIBC) remains unclear. Aim of the present study was to investigate the combined and stepwise use of the four most broadly available urine markers to detect tumor recurrence in patients undergoing surveillance of NMIBC. PATIENTS AND METHODS: 483 patients with history of NMIBC were included. Cytology, UroVysion, fluorescence in situ hybridization (FISH), immunocytology (uCyt+), and NMP22 ELISA were performed before surveillance cystoscopy. Characteristics of single tests and combinations were assessed by contingency analysis. RESULTS: 128 (26.5%) patients had evidence of tumor recurrence. Sensitivities and negative predictive values (NPVs) of the single tests ranged between 66.4-74.3 and 82.3-88.2%. Two-marker combinations showed sensitivities and NPVs of 80.5-89.8 and 89.5-91.2%. A stepwise application of the two-test combinations with highest accuracy (cytology and FISH; cytology and uCyt+; uCyt+ and FISH) showed NPVs for high-risk recurrences (G3/Cis/pT1) of 98.8, 98.8, and 99.1%, respectively. CONCLUSIONS: Combinations of cytology, FISH, immunocytology, and NMP22 show remarkable detection rates for recurrent NMIBC. Stepwise two-test combinations of cytology, FISH, and immunocytology have a low probability of missing a high-risk tumor. The high sensitivities may justify the use of these combinations in prospective studies assessing the use of urine markers to individualize intervals between cystoscopies during follow-up.
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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 | 0.000 | 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