Urinary cytology and nuclear matrix protein 22 in the detection of bladder cancer recurrence other than transitional cell carcinoma
Bibliographic record
Abstract
OBJECTIVE: To assess the value of nuclear matrix protein-22 (NMP22), compared with urinary cytology, in predicting the recurrence of bladder cancer that is not transitional cell carcinoma (non-TCC). PATIENTS AND METHODS: We tested the sensitivity, specificity and the predictive accuracy of NMP22 in the context of non-TCC bladder cancer recurrence, and compared it to the performance of urinary cytology. The study group comprised 2687 patients with history of non-muscle-invasive bladder cancer from 10 centres across four continents. RESULTS: The mean patient age was 64.8 years and 75.4% were men; of all patients, 513 (19.1%) had positive urinary cytology, 906 (33.7%) had a positive NMP22 test (>or=10 units/mL) and 80 (3.0%) had non-TCC recurrence. Most of these, i.e. 60 (75%), were stage >or=T2. The sensitivity and specificity of urinary cytology were, respectively, 20.0% and 94.8%, vs 77.5% and 81.8% for NMP22 of >or=10 units/mL. The predictive accuracy of urinary cytology was 57.5%, vs 87.1% for NMP22 >or= 10 units/mL. A combined model that included dichotomized NMP22 and urinary cytology was 85.3% accurate. CONCLUSION: The ability of a NMP22 level of >or=10 units/mL to predict non-TCC recurrence was better than that of urinary cytology, suggesting that NMP22 might have a role in the surveillance of patients at risk of non-TCC recurrence.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".