Nomograms including the UBC <sup>®</sup> Rapid test to detect primary bladder cancer based on a multicentre dataset
Bibliographic record
Abstract
Objectives To evaluate the clinical utility of the urinary bladder cancer antigen test UBC ® Rapid for the diagnosis of bladder cancer (BC) and to develop and validate nomograms to identify patients at high risk of primary BC. Patients and Methods Data from 1787 patients from 13 participating centres, who were tested between 2012 and 2020, including 763 patients with BC, were analysed. Urine samples were analysed with the UBC ® Rapid test. The nomograms were developed using data from 320 patients and externally validated using data from 274 patients. The diagnostic accuracy of the UBC ® Rapid test was evaluated using receiver‐operating characteristic curve analysis. Brier scores and calibration curves were chosen for the validation. Biopsy‐proven BC was predicted using multivariate logistic regression. Results The sensitivity, specificity, and area under the curve for the UBC ® Rapid test were 46.4%, 75.5% and 0.61 (95% confidence interval [CI] 0.58–0.64) for low‐grade (LG) BC, and 70.5%, 75.5% and 0.73 (95% CI 0.70–0.76) for high‐grade (HG) BC, respectively. Age, UBC ® Rapid test results, smoking status and haematuria were identified as independent predictors of primary BC. After external validation, nomograms based on these predictors resulted in areas under the curve of 0.79 (95% CI 0.72–0.87) and 0.95 (95% CI: 0.92–0.98) for predicting LG‐BC and HG‐BC, respectively, showing excellent calibration associated with a higher net benefit than the UBC ® Rapid test alone for low and medium risk levels in decision curve analysis. The R Shiny app allows the results to be explored interactively and can be accessed at www.blucab‐index.net . Conclusion The UBC ® Rapid test alone has limited clinical utility for predicting the presence of BC. However, its combined use with BC risk factors including age, smoking status and haematuria provides a fast, highly accurate and non‐invasive tool for screening patients for primary LG‐BC and especially primary HG‐BC.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".