FGFR3 mutation as a prognostic indicator in patients with urothelial carcinoma: A systematic review and meta-analysis.
Bibliographic record
Abstract
411 Background: Mutations in the fibroblast growth factor receptor-3 (FGFR3) have been implicated in urothelial tumorigenesis. The role of FGFR3 inhibitors in urothelial carcinoma is being explored in clinical trials. Here we explore the association between FGFR3 mutations and survival in urothelial carcinoma. Methods: We conducted a systemic review of electronic databases to identify studies published 1985-2018. Studies were included if they described the associated between FGFR3 mutations and outcomes of non-muscle invasive (NMI) and muscle invasive (MI) urothelial carcinomas. We used a composite endpoint of progression-free and recurrence-free survival (PRFS). Analysis was performed in Revman software. Hazard ratios (HR) and the 95% confidence intervals (CI) were obtained and entered; and then weighted and pooled in a meta-analysis with random effect modelling. The statistical tests were two sided. Results: Twelve retrospective and prospective studies comprising a total of 2162 patients were included. Analysis was done for two groups. The first group, included 1651 patients with NMI urothelial carcinomas; 886 (53.6%) of these had FGFR3 mutation. Compared to FGFR3 wild type, FGFR3 mutation did not influence PRFS (HR = 1.01, CI = 0.79-1.29, p = 0.95); I2 42%. In the second analysis, 511 patients with NMI and MI urothelial carcinomas were evaluated; 30% (n = 151) of which had FGFR3 mutation. In this group, FGFR3 mutation was not associated with PRFS (HR = 1.46, CI = 0.45-4.71, p = 0.53); I2 90%. Conclusions: Our meta-analysis does not show an association between FGFR3 mutation status and PRFS in urothelial carcinoma. [Table: see text]
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.028 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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 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".