Body composition and endometrial cancer outcomes
Bibliographic record
Abstract
BACKGROUND: Obesity is a known risk factor for developing endometrial cancer. However, the association of obesity with endometrial cancer (EC) outcomes has not been clearly established. This study examined how outcomes in women with early stage EC vary with body composition measured via computed tomography (CT). METHODS: In this retrospective study, patients diagnosed with EC international Federation of Gynecology and Obstetrics stages I-III and available CT scans were included. Automatica software was used to assess the areas of visceral adipose tissue, subcutaneous adipose tissue (SAT), and intermuscular adipose tissue (IMAT) and skeletal muscle area. RESULTS: Of 293 patient charts assessed, 199 met eligibility criteria. Median body mass index (BMI) was 32.8 kg/m2 (interquartile range [IQ] = 26.8-38.9); 61.8% had histologic subtype endometrioid carcinoma. Adjusted for age, international Federation of Gynecology and Obstetrics stage, and histologic subtype, a BMI of at least 30 vs less than 30 kg/m2 was associated with lower endometrial cancer-specific survival (ECSS) (hazard ratio [HR] = 2.32, 95% confidence interval [CI] = 1.27 to 4.25) and overall survival (OS) (HR = 2.7, 95% CI = 1.35 to 5.39). Higher IMAT 75th vs 25th percentile and SAT of at least 225.6 vs less than 225.6 cm2 were associated with lower ECSS (HR = 1.53, 95% CI = 1.1 to 2.13, and HR = 2.57, 95% CI = 1.13 to 5.88) and OS (HR = 1.50, 95% CI = 1.11 to 2.02, and HR = 2.46, 95% CI = 1.2 to 5.01), respectively. The association of visceral adipose tissue (75th vs 25th percentile) with ECSS and OS was not statistically significant (HR = 1.42, 95% CI = 0.91 to 2.22, and HR = 1.24, 95% CI = 0.81 to 1.89). CONCLUSION: Higher BMI, IMAT, and SAT were associated with higher mortality from EC and lower OS. A better understanding of the mechanisms underlying these relationships could inform strategies to improve patient outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".