CT-based body composition and its demographic and clinical associations in women aged 20 to 40 with non-metastatic breast cancer
Bibliographic record
Abstract
This cross-sectional study investigated the relationship between CT-based body composition parameters and demographic and clinical factors in young women (< 40 years) with non-metastatic (I–III) breast cancer. Data on anthropometry, sociodemographic characteristics, and tumor profiles were extracted from medical records. Body composition was assessed using CT scans at the third lumbar vertebra (L3), measuring skeletal muscle cross-sectional area (SM), skeletal muscle index (SMI), skeletal muscle radiodensity (SMD), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). A total of 173 patients were included (mean age: 33.8 ± 4.1 years). Most women had stage III tumors and hormone receptor-positive status. Higher VAT levels were associated with older age (P = 0.001). A higher education level was associated with higher SAT values. Women with invasive ductal carcinoma or positive estrogen receptor status were less likely to have high VAT. Patients with high VAT and SAT had higher BMI, SM, and SMI, and lower SMD (all P < 0.005). Our findings suggest that SAT and VAT are associated with educational level and tumor aggressiveness, respectively, highlighting the importance of monitoring body composition as part of clinical care in young women with breast 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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".