Impact of clinical factors, including a point‐of‐care nuclear matrix protein‐22 assay and cytology, on bladder cancer detection
Bibliographic record
Abstract
OBJECTIVE: To determine whether the nuclear matrix protein-22 (NMP22) assay can improve the accuracy of discriminating between high-risk patients with and without bladder cancer. PATIENTS AND METHODS: Age, gender, race, smoking status, haematuria and its extent, and the NMP22 and urinary cytology results, were available for 1272 patients. The data of 670 (52.7%) from four study sites were used to develop a logistic regression model-based nomogram to predict the presence of bladder cancer. The remaining data from 602 (47.3%) patients from nine study sites were used to externally validate the nomogram. A separate nomogram was developed for urinary cytology, and for the combination of NMP22 and urinary cytology findings. RESULTS: Of 1272 patients, 76 (6.0%) had bladder cancer, 217 (17.1%) were NMP22-positive and 17 (1.3%) had malignant cells on urinary cytology. NMP22 and urinary cytology results were independent predictors of bladder cancer (P = 0.005 and 0.007, respectively). In external validation, the area under the curve (AUC) for NMP22 was 76.0% vs 56.2% for cytology. External validation of the multivariable NMP22-based bladder cancer nomogram gave an AUC of 82.4% vs 74.7% for the multivariable cytology-based nomogram (gain 7.7%; P = 0.006) vs 82.6% for the multivariable nomogram combining NMP22 and cytology results (gain 0.2%; P = 0.1). CONCLUSIONS: The ability of the NMP22 test to predict bladder cancer in high-risk patients significantly exceeds that of urinary cytology. The NMP22-based nomogram can help to identify individuals at risk of bladder cancer.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".