Visceral to subcutaneous fat area ratio predicts early postoperative small bowel obstruction after total gastrectomy for cardia cancer
Bibliographic record
Abstract
OBJECTIVE: We aimed to investigate the relationship between the visceral to subcutaneous fat area ratio (V/S ratio) and incidence of early postoperative small bowel obstruction (EPSBO) following total gastrectomy for cardia cancer. METHODS: We conducted a retrospective analysis among patients with cardia cancer who underwent elective total gastrectomy with esophagojejunostomy Roux-en-Y anastomosis at Nanjing Yimin Hospital between November 2019 and April 2024. Preoperative, intraoperative, and postoperative factors were meticulously monitored. The V/S ratio was calculated using computed tomography scans at the umbilical level with Slice-O-Matic software (Tomovision, Montreal, Canada). Statistical analyses included logistic regression and receiver operating characteristic (ROC) curve analysis. RESULTS: Among 175 patients, 27 (15.4%) developed EPSBO. The V/S ratio was significantly higher in the EPSBO group (1.76 ± 1.05 vs. 1.01 ± 0.54). Logistic regression identified the V/S ratio as a significant predictor of EPSBO (odds ratio [OR] = 1.612, 95% [CI]: 1.102-1.605). ROC curve analysis demonstrated high sensitivity (92%) and specificity (100%) for the V/S ratio in predicting EPSBO, with a 0.83 AUC. CONCLUSIONS: Our findings indicated a higher V/S ratio was a significant predictor of EPSBO following total gastrectomy for cardia cancer. Preoperative assessment of the V/S ratio can inform risk stratification and guide targeted interventions to improve postoperative 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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".