Multiple biomarkers improve prediction of bladder cancer recurrence and mortality in patients undergoing cystectomy
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Bibliographic record
Abstract
BACKGROUND: Tested was whether the assessment of 5 established bladder cancer biomarkers (p53, pRB, p21, p27, and cyclin E1) could improve the ability to predict disease recurrence and cancer-specific survival after radical cystectomy in patients with pTa-3N0M0 urothelial carcinoma of the bladder (UCB). METHODS: The study comprised 191 patients with pTa-3N0M0 UCB treated with radical cystectomy and bilateral lymphadenectomy (median follow-up, 3.1 years). Biomarker expression was assayed on serial tissue microarray slides using quantitative immunohistochemistry using advanced cell imaging and color detection software. Predictive accuracy was quantified using the concordance index and 200-bootstrap resamples were used to reduce overfit bias. Bootstrap-adjusted predictive accuracy estimates were compared using the Mantel-Haenszel test. RESULTS: UCB recurred in 36 (18.8%) patients and 30 (15.7%) died of bladder cancer; 157 (82.2%) patients had altered expression of at least 1 biomarker. In univariate analyses the number of altered biomarkers had the highest predictive accuracy for both disease recurrence (76.8%, P< .001) and cancer-specific mortality (78.3%, P< .001). Addition of the number of altered biomarkers increased the predictive accuracy of nomograms based on the TNM staging system for disease recurrence and cancer-specific mortality by 10.9% (83.4% vs 72.5%, P< .001) and 8.6% (86.9% vs 78.3, P< .001), respectively. CONCLUSIONS: Assessment of the number of altered biomarkers in the cystectomy specimen improves the prediction of bladder cancer recurrence and survival in patients with pTa-3N0M0 disease. Prospective evaluation of alteration in these biomarkers can help identify patients who would benefit from adjuvant treatment after radical cystectomy.
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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