Prognostic Effects of Sarcopenia on Patients with Bladder Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Sarcopenia can negatively impact the survival of cancer patients. This study intends to delve into the correlation of sarcopenia with survival and complications in patients with bladder cancer (BC) after surgery. Web of Science, Cochrane Library, Embase, and PubMed databases were retrieved up to April 7, 2023, to collect studies on the impact of sarcopenia on the prognosis of adults with BC. Primary outcomes encompassed overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS). The secondary outcome consisted of postoperative complications. A meta-analysis was conducted using Stata. Forest plots and summary effect models were employed to present the results. The quality of eligible studies was assessed using the Newcastle–Ottawa Scale (NOS). Initially, 1713 studies were identified through searches across four databases, and 26 studies were ultimately included in the analysis. Sarcopenia was significantly associated with OS (HR:1.62; 95% CI: 1.43–1.83; P < 0.001, I2 = 0.9%), CSS (HR: 1.81, 95% CI: 1.52–2.15, P < 0.001, I2 = 0.0%), and RFS (HR: 1.76, 95% CI: 1.21–2.56, P = 0.003, I2 = 0.0%) in BC patients. Subgroup analyses revealed that sarcopenia is strongly linked to prognosis and postoperative complications in BC patients.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.039 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".