Association between sarcopenia and survival in patients with gynecologic cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
Background Despite prior attempts to evaluate the effects of sarcopenia on survival among patients with gynecologic cancer, the results of these studies have not been consistent. The present study evaluated the association between sarcopenia and survival among patients with gynecologic cancer by aggregating multiple studies. Methods We performed a literature search using computerized databases and identified additional studies included in the bibliographies of retrieved articles. The quality of each study was evaluated using the Newcastle–Ottawa Scale, and meta-analyses were performed to evaluate overall survival (OS) and progression-free survival (PFS). We constructed a forest plot for each outcome and assessed publication bias using Begg’s test. Heterogeneity was assessed using I2 statistics. Results From the 5,933 initially identified articles, 16 studies describing 2,031 participants with a mean age of 60.34 years were included in the meta-analysis. We found that compared with patients with gynecologic cancer but without sarcopenia, patients with sarcopenia had worse OS, with a pooled hazard ratio (HR) of 2.61 (95% confidence interval [CI]:1.52–4.46), and worse PFS (HR: 1.37, 95% CI: 1.09–1.73). The quality of studies was generally good, and no publication bias was detected among studies for either OS or PFS. Although 4 of 12 studies were of fair quality, we conducted a sensitivity analysis excluding studies or fair quality and obtained similar results. Conclusions These meta-analysis results suggest that sarcopenia is associated with worse OS and PFS among patients with gynecologic cancer. The use of different case definitions appeared to be a major source of heterogeneity among the studies. Further studies remain necessary to confirm our findings, especially those examining OS and PFS, because publication bias was identified.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.043 |
| Bibliometrics | 0.010 | 0.010 |
| 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".