Low muscularity increases the risk for post‐operative pneumonia and delays recovery from complications after oesophago‐gastric cancer resection
Bibliographic record
Abstract
BACKGROUND: Low muscularity is associated with adverse surgical outcomes. We aimed to determine whether low muscularity is associated with an increased risk of post-operative complications and reduced long-term survival after oesophago-gastric cancer surgery. METHODS: Patients who underwent radical oesophago-gastric cancer surgery with preoperative abdominal computed tomography (CT) imaging were included. Low skeletal muscle index (SMI), measured by CT, was determined using pre-defined cut-points. Oncological, surgical, complications and outcome data were obtained from a prospective database. RESULTS: Of 108 patients, 61% (n = 66) had low SMI preoperatively. Patients with low SMI had a higher rate of post-operative pneumonia (30 vs. 7% normal muscularity, P = 0.004). Median length of stay (LOS) was higher in patients with low SMI if they had any complication (19.5 vs. 14 days, P = 0.026) or pneumonia (21 vs. 13 days, P = 0.018). On multivariate analysis, low SMI (OR 3.85, CI 1.10-13.4, P = 0.025), preoperative weight loss (OR 1.13, CI 1.01-1.25, P = 0.027), and smoking (OR 5.08, CI 1.24-20.9, P = 0.024) were independent predictors of having a severe complication. There was no difference in 5-year overall (62% vs. 69%, P = 0.241) and disease-free (11% vs. 21.4%, P = 0.110) survival between low SMI and normal muscle mass groups. CONCLUSION: Low SMI is associated with a significantly increased risk of pneumonia and increased LOS for patients with complications. Assessment of muscle mass may require additional muscle quality, strength, and physical performance measures to enhance preoperative risk assessment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".