The prognostic impact of myosteatosis on overall survival in gynecological cancer patients: A meta‐analysis and trial sequential analysis
Bibliographic record
Abstract
Myosteatosis is a novel imaging biomarker for survival in gynecological cancer patients; however, the evidence is inconsistent. This meta-analysis aims to investigate the impact of myosteatosis on overall survival in the gynecological oncology setting. Three databases (PubMed, EMBASE and Web of Science) were systematically searched for relevant literature up to October 30, 2021. A random-effects model was used to evaluate the predictive effect of myosteatosis on overall survival in the gynecological cancer population. The Newcastle-Ottawa Scale was used to assess the methodological quality of the included studies. Trial sequential analysis was used to control the risk of random errors. Twelve studies with a total of 2519 patients were included. Myosteatosis was associated with a 50% increased mortality risk (HR 1.50, 95% CI 1.24-1.82, P < .001) in gynecological cancer patients. Subgroup analyses stratified by study design, statistical model, treatment, sample size and stage confirmed the predictive value of myosteatosis on survival. However, the prognostic ability of myosteatosis only was held in the American and European populations but lost in Asians. Additionally, myosteatosis was not associated with the increased mortality in endometrial and cervical cancers, except for ovarian cancers. Overall, myosteatosis is a powerful predictor of reduced overall survival in gynecological cancer 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 | 0.019 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.044 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".