Low L3 skeletal muscle index associated with the clinicopathological characteristics and prognosis of ovarian cancer: a meta‐analysis
Bibliographic record
Abstract
Sarcopenia is a syndrome characterized by progressive loss of skeletal muscle mass, strength and function, which is one of the most important clinical features of cancer malnutrition, representing a poor prognostic indicator in oncology. Sarcopenia is commonly assessed by measuring the skeletal muscle index (SMI) of the third lumbar spine (L3) using computed tomography (CT). The primary aim of this meta-analysis was to study the association between low SMI and comprehensive clinicopathological characteristics as well as prognosis in patients with ovarian cancer. Data were searched in PubMed, EMBASE and Cochrane databases from inception to 10 June 2022. Studies evaluating the prognostic effect of SMI on ovarian cancer survival or chemotherapy-related side effects were included. The risk of bias and study quality were assessed via the Newcastle-Ottawa Scale (NOS). The search strategy yielded 1286 hits in all three databases combined. Thirteen studies were included for qualitative and quantitative analysis, comprising 1814 patients. Our meta-analysis revealed the significant association between low SMI and progression-free survival (PFS) [P = 0.02; hazard ratio (HR): 1.40, 95% confidence interval (CI): 1.06-1.85], as well as 5-year overall survival (OS) [P = 0.02; odds ratio (OR): 1.35, 95% CI: 1.05-1.74]. Low SMI was also obviously associated with body mass index (BMI) < 25 (P < 0.00001; OR: 5.08, 95% CI: 3.54-7.30), FIGO stage (P = 0.02; OR: 1.62, 95% CI: 1.06-2.45) and R0 cytoreduction (P = 0.04;OR: 1.34, 95% CI: 1.01-1.79). There was no correlation between low SMI and histological types, pathological grades and chemotherapy-related toxicity. The quality of the evidence was relatively high according to NOS. Our meta-analysis indicated that sarcopenia assessed by SMI was associated with poor clinical characteristics and adverse prognosis in patients with ovarian cancer. Consensus should be reached on standardized cut-off values for defining sarcopenia in patients with ovarian cancer.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.035 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".