The Impact of Computed Tomography Measurements of Sarcopenia on Postoperative and Oncologic Outcomes in Patients Undergoing Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy
Bibliographic record
Abstract
Cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS-HIPEC) is a treatment option for peritoneal metastases (PM) but is associated with significant postoperative morbidity. The aim of this study was to determine the prognostic value of computed tomographic (CT)-measured sarcopenia on postoperative outcomes and survival in patients undergoing CRS-HIPEC for PM from various origins. A retrospective cohort study was conducted between 2012 and 2020. Three-hundred and twelve patients (mean age 57.6 ± 10.3, 34.3% male) were included, of which 88 (28.2%) were sarcopenic. PM from a colorectal origin was the most common in both groups. The proportion of major postoperative complications (Clavien-Dindo ≥ III) was not higher in the sarcopenic group (15.9% in sarcopenic patients vs. 23.2% in nonsarcopenic patients, p = 0.17). The mean Comprehensive Complication Index scores, HIPEC-related toxicities, length of hospital stay, and duration of parenteral nutrition were comparable regardless of sarcopenia status. In the multivariate logistic regression analysis of severe complications, only peritoneal carcinomatosis index reached statistical significance (OR, 1.05; 95% CI, 1.01 to 1.08, p = 0.007). Sarcopenia did not impact origin-specific overall survival on Cox regression analysis. Sarcopenia was not associated with worse rates of postoperative severe complications or worse survival rates. Future prospective studies are required before considering sarcopenia as part of preoperative risk assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".