Computed Tomography–Quantified Body Composition Predicts Short-Term Outcomes After Gastrectomy in Gastric Cancer
Bibliographic record
Abstract
Background: Malnutrition is a common and critical problem that influences outcome in cancer patients. Body composition reflects a patient’s metabolic profile and physiologic reserves, which might be the true determinant of prognosis. In the present study, which aimed to identify valuable new prognostic indicators, we investigated the association between computed tomography–quantified body composition and short-term outcomes after gastrectomy for gastric cancer. Methods: Skeletal muscle index, mean muscle attenuation, and ratio of visceral-to-subcutaneous adipose tissue area (VSR) were calculated from preoperative computed tomography images. Low skeletal muscle index, low mean muscle attenuation, and high VSR were respectively termed “sarcopenia,” “myosteatosis,” and “visceral obesity.” The association of body composition with postoperative complications and serum markers of nutrition and inflammation after radical gastrectomy were analyzed. Results: The overall complication rate was significantly higher in the sarcopenia (62.5% vs. 27.3%, p = 0.001) and myosteatosis groups (38.2% vs. 4%, p = 0.002). Patients with visceral obesity had a higher incidence of inflammatory complications (20.3% vs. 6.5%, p = 0.01). Multivariate logistic regression analysis demonstrated that sarcopenia (p = 0.013), myosteatosis (p = 0.017), and low serum retinol-binding protein (p = 0.019) were independent risk factors for overall complications. Compared with control subjects, patients with sarcopenia had lower postoperative levels of serum retinol-binding protein (p = 0.007), and patients with visceral obesity had higher levels of C-reactive protein (p = 0.026). Conclusions: Sarcopenia, myosteatosis, and visceral obesity were significantly associated with increased rates of postoperative complications and affected the postoperative nutrition and inflammation status of patients with gastric cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".