North American study and meta-analysis evaluating performance of Bladder EpiCheck <sup>®</sup> , a FDA cleared test, in non-muscle invasive bladder cancer recurrence
Bibliographic record
Abstract
Background: Bladder EpiCheck (BE) is a novel methylation-based PCR urine test for the detection of non-muscle invasive bladder cancer (NMIBC) recurrences. Objective: We present the results of a North American study evaluating BE and meta-analysis of literature. Methods: A prospective, blinded, multicenter study was conducted in North America. Voided urine was collected from NMIBC patients prior to cystoscopic surveillance. BE testing was performed centrally. For the meta-analysis, a PUBMED search was performed to identify all published peer-reviewed clinical studies of BE for NMIBC surveillance. Results: In this study, 674 patients were enrolled of which 449 were included. Overall sensitivity was 67% (95%CI 58%-74%), specificity was 84% (80%-88%), PPV was 65% (57%-73%) and NPV was 85% (81%-89%). For high-grade (HG) recurrence, sensitivity was 77% (65%-85%) and NPV was 95% (92%-97%).In patients with negative cystoscopy and cytology at the first study visit, risk of subsequent recurrence in 12 months was 5.3 (2.7-10.3) times higher in patients with positive BE vs. negative BE (p < 0.0001). In patients with negative cystoscopy and equivocal cytology, BE was positive in 75-89% of those with later HG recurrence, with PPV of 42% (15%-72%)-63% (38%-84%).The meta-analysis included 7 studies and 1564 patients. Overall sensitivity was 82% (66-92%), HG sensitivity was 91% (82-95%), specificity was 85% (80-88%), PPV was 60% (55-64%) and HG NPV was 98% (97-99%). Conclusions: The consistently strong performance of BE indicate that a positive test could improve timely disease recurrence detection and a negative test could rule-out HG disease. Furthermore, the low rate of false positive results, potentially minimizes unnecessary downstream procedures and patient anxiety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".