The Prognostic Value of Sarcopenia in Clinical Outcomes in Cervical Cancer: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Sarcopenia is a condition characterized by inadequate muscle and function decline and is often associated with ageing and cancer. It is established that sarcopenia and muscle loss occurred during treatment are associated with the clinical outcomes of patients with cancer. This systematic review and meta-analysis aims to evaluate the association between sarcopenia at pretreatment and during treatment and overall survival or disease progression in patients with cervical cancer. METHODS: The Web of Science, Embase, Medline and Cochrane Library databases were searched until 4 July 2024. Studies evaluating the prognostic effect of muscle mass at pretreatment or muscle change during treatment on survival or disease progression for patients with cervical cancer were included. Study quality was evaluated with the Newcastle-Ottawa Scale (NOS). Forest plots and summary effect models were used to show the effect size of sarcopenia on clinical outcomes. RESULTS: The search strategy yielded 1721 studies in four databases. Eleven and seven studies were included in the quantitative analysis of pretreatment sarcopenia and muscle change on clinical outcomes, respectively. A total of 1907 patients underwent pretreatment muscle assessment, but 1016 were monitored for muscle changes; however, none of the studies involved measures of muscle strength or function. Meta-analysis showed a significant association between pretreatment sarcopenia and OS [hazard ratio (HR) 1.58, 95% confidence interval (CI): 1.16-2.14, p = 0.003] and PFS (HR 1.63, 95%CI 1.16-2.29, p = 0.005) according to data of univariate analysis. In the meta-analysis of the multivariate data, pretreatment sarcopenia remained associated with poor OS (HR 3.09, 95% CI: 2.07-4.61, p < 0.00001) and PFS (HR: 1.55, 95%CI 1.06-2.28, p = 0.03). Additionally, muscle loss was significantly associated with OS (HR 5.18, 95%CI 3.54-7.56, p < 0.00001) and PFS (HR 2.62, 95%CI 1.63-4.22, p < 0.00001). Subgroup analysis showed that the association between pretreatment sarcopenia and OS, as well as PFS, was influenced by muscle mass measurements and cut-off values, whereas muscle loss consistently predicted worse OS and PFS when stratified by varying degrees of reduction. The NOS scores of all included studies were ≥ 6. CONCLUSIONS: Pretreatment sarcopenia and muscle change during treatment are significantly associated with both overall survival and disease progression. Therefore, muscle assessment and monitoring should be conducted for appropriate diagnosis and intervention to improve clinical outcomes in patients with cervical cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".