Sarcopenia is associated with postoperative infection and delayed recovery from colorectal cancer resection surgery
Bibliographic record
Abstract
BACKGROUND: Skeletal muscle depletion (sarcopenia) predicts morbidity and mortality in the elderly and cancer patients. METHODS: We tested whether sarcopenia predicts primary colorectal cancer resection outcomes in stage II-IV patients (n=234). Sarcopenia was assessed using preoperative computed tomography images. Administrative hospitalisation data encompassing the index surgical admission, direct transfers for inpatient rehabilitation care and hospital re-admissions within 30 days was searched for International Classification of Disease (ICD)-10 codes for postoperative infections and inpatient rehabilitation care and used to calculate length of stay (LOS). RESULTS: Overall, 38.9% were sarcopenic; 16.7% had an infection and 9.0% had inpatient rehabilitation care. Length of stay was longer for sarcopenic patients overall (15.9 ± 14.2 days vs 12.3 ± 9.8 days, P=0.038) and especially in those ≥ 65 years (20.2 ± 16.9 days vs 13.1 ± 8.3 days, P=0.008). Infection risk was greater for sarcopenic patients overall (23.7% vs 12.5%; P=0.025), and especially those ≥ 65 years (29.6% vs 8.8%, P=0.005). Most (90%) inpatient rehabilitation care was in patients ≥ 65 years. Inpatient rehabilitation was more common in sarcopenic patients overall (14.3% vs 5.6%; P=0.024) and those ≥ 65 years (24.1% vs 10.7%, P=0.06). In a multivariate model in patients ≥ 65 years, sarcopenia was an independent predictor of both infection (odds ratio (OR) 4.6, (95% confidence interval (CI) 1.5, 13.9) P<0.01) and rehabilitation care (OR 3.1 (95% CI 1.04, 9.4) P<0.04). CONCLUSION: Sarcopenia predicts postoperative infections, inpatient rehabilitation care and consequently a longer LOS.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".