Prognostic value of preoperative hematologic biomarkers in urothelial carcinoma of the bladder treated with radical cystectomy: a systematic review and meta-analysis
Bibliographic record
Abstract
This systematic review and meta-analysis aimed to assess the prognostic value of preoperative hematologic biomarkers in patients with urothelial carcinoma of the bladder treated with radical cystectomy. PUBMED, Web of Science, Cochrane Library, and Scopus databases were searched in September 2019 according to the Preferred Reporting Items for Systematic Review and Meta-analysis statement. Studies were deemed eligible if they compared cancer-specific survival in patients with urothelial carcinoma of the bladder with and without pretreatment laboratoryabnormalities. Formal meta-analyses were performed for this outcome. The systematic review identified 36 studies with 23,632 patients, of these, 32 studies with 22,224 patients were eligible for the meta-analysis. Several preoperative hematologic biomarkers were significantly associated with cancer-specific survival as follows: neutrophil - lymphocyte ratio (pooled hazard ratio [HR]: 1.20, 95% confidence interval [CI]: 1.11-1.29), hemoglobin (pooled HR: 0.87, 95% CI 0.82-0.94), C-reactive protein (pooled HR: 1.44, 95% CI 1.26-1.66), De Ritis ratio (pooled HR: 2.18, 95% CI 1.37-3.48), white blood cell count (pooled HR: 1.05, 95% CI 1.02-1.07), and albumin-globulin ratio (pooled HR: 0.26, 95% CI 0.14-0.48). Several pretreatment laboratory abnormalities in patients with urothelial carcinoma of the bladder were associated with cancer-specific mortality. Therefore, it might be useful to incorporate such hematologic biomarkers into prognostic tools for urothelial carcinoma of the bladder. However, given the study limitations including heterogeneity and retrospective nature of the primary data, the conclusions should be interpreted with caution.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".