Role of TERT mutations in bladder cancer prognosis: Protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Background: One of the greatest obstacles to improving long-term survival rates for bladder cancer patients is the disease's rapid recurrence rate coupled with aggressive metastatic behavior. Discovering effective prognostic markers is necessary to improve treatment results. Objective: This systematic review sets out to establish the effectiveness of TERT mutations as prognostic markers for disease free survival (DFS) and overall survival (OS) and recurrence-Free Survival (RFS) and progression-free survival (PFS) and disease-specific survival rate (DSS) of patients with bladder cancer by collating findings from various studies. Methods: Quality of selected articles will be evaluated using the Newcastle-Ottawa Scale (NOS) checklist independently by two reviewers to determine risk of bias. Differences will be sorted through a discussion with a a third reviewer. STATA 12.0 will be utilized to do the synthesis of data by estimating hazard ratios (HRs) and 95% confidence intervals (CIs) for TERT mutations. Heterogeneity will be determined by Higgins’ I^2 statistic, and where there is considerable heterogeneity a random effects model will be used. Begg’s funnel plot and Egger’s test, together with a sensitivity analysis, will carry out evaluation of the publication bias. Ethnics and dissemination: since this is a review of existing research, ethical approval is not required. The findings of this systematic review and meta-analysis will be shared through publication in a peer?reviewed journal. PROSPERO registration number: CRD42024596965
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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 | 0.067 | 0.105 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.066 | 0.007 |
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".