Survivin as a Prognostic Marker for Urothelial Carcinoma of the Bladder: A Multicenter External Validation Study
Bibliographic record
Abstract
PURPOSE: The aim of the current study was to externally validate the value of survivin as a prognostic marker for bladder cancer in a large multi-institutional cohort of patients treated with radical cystectomy. METHODS: The study comprised 726 patients treated with radical cystectomy and bilateral pelvic lymphadenectomy. Survivin staining and scoring were done with automated systems coupled with advanced color detection software. Specimens showing at least 10% reactivity were considered altered. Predictive accuracy was quantified using the concordance index and 200-bootstrap resamples were used to reduce overfit bias. RESULTS: Survivin was an independent predictor of disease recurrence and cancer-specific survival in multivariable analyses that controlled for the effects of standard clinicopathologic features (hazard ratios, approximately 1.6; P values < or = 0.002). In all patients (n = 726), addition of survivin to a model including standard clinicopathologic variables did not improve its predictive accuracy (P = 0.67 for disease recurrence and P = 0.27 for cancer-specific survival). In the subgroup of patients with pT(1-3)N(0)M(0) disease (n = 398), addition of survivin improved the accuracy of standard clinicopathologic features for prediction of disease recurrence and cancer-specific survival (1.3%, P < 0.001 and 1.2%, P < 0.001, respectively). CONCLUSIONS: Survivin expression improves our accuracy for prediction of cancer recurrence and survival in pT(1-3)N(0)M(0) patients by a small but statistically significant margin. Our findings support the need for further evaluation of survivin and its signaling pathways as well as survivin-targeted therapies in bladder cancer.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".