Low L3 skeletal muscle index and endometrial cancer: a statistic pooling analysis
Bibliographic record
Abstract
OBJECTIVE: Sarcopenia, a condition characterized by the gradual decline of muscle mass, strength, and function, is a key indicator of malnutrition in cancer patients and has been linked to poor prognoses in oncology. Sarcopenia is commonly assessed by measuring the skeletal muscle index (SMI) of the third lumbar spine (L3) using computed tomography (CT). This meta-analysis aimed to explore the relationship between low SMI and clinicopathological features, as well as prognosis, in individuals with endometrial cancer (EC). METHODS: Data from various databases including PubMed, Embase, Cochrane, Medline, and Web of Science were searched up until October 20th, 2024. Studies that investigated the association of low SMI and EC survival or clinicopathological characteristics were included. Pooled effect sizes were reported as hazards ratio (HR), odds ratios (ORs) or weighted mean difference (WMD). The quality and risk of bias in the studies were evaluated using the Newcastle-Ottawa Scale (NOS) and the Quality In Prognosis Studies (QUIPS), and the study was registered on PROSPERO (CRD42024509949) before commencing the search. RESULTS: A total of 218 studies were identified across all five databases, with 11 studies meeting the criteria for qualitative and quantitative analysis, involving 1588 patients. The findings of our meta-analysis demonstrated a significant link between low SMI and progression-free survival [P = 0.002; HR: 1.62, 95% CI: 1.20-2.17]. Low SMI was also associated with a BMI < 25 (P < 0.00001; OR: 4.55, 95% CI: 3.01-6.87), FIGO stage (P = 0.04; OR: 1.33, 95% CI: 1.01-1.75), pathology grades (P = 0.001; OR: 1.77, 95% CI: 1.26-2.49), and the endometrioid pathological type (P = 0.01; OR: 0.68, 95% CI: 0.51-0.92). However, no significant correlation was found between low SMI and 5-year overall survival, serous pathological type, recurrence, length of hospital stay, intraoperative complications, and postoperative complications. All the included studies scored ≥ 7 on the NOS, indicating relatively high-quality evidence. CONCLUSIONS: The meta-analysis highlighted the association between low SMI and unfavorable clinical features and outcomes in EC patients, emphasizing the importance of early diagnosis and appropriate management of sarcopenia assessed by low SMI to enhance prognoses in EC 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 | 0.059 | 0.094 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.069 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".