The prognostic value of skeletal muscle index on clinical and survival outcomes after cytoreduction and HIPEC for peritoneal metastases from colorectal cancer: A systematic review and meta-analysis.
Bibliographic record
Abstract
BACKGROUND: Cytoreductive surgery (CRS) and hypertermic intraperitoneal chemotherapy (HIPEC) represent the most effective strategy to manage peritoneal metastases (PM). This systematic review and meta-analysis aimed to assess the impact of body composition on clinical outcomes in patients with PM. METHODS: August 2020. Data were independently extracted by 3 authors. Newcastle-Ottawa Scale was used to assess quality and risk of bias of studies. Pooled analyses were performed using Mantel-Haenszel method to estimate overall effect size with mean differences or odd ratios (ORs) and 95% confidence interval (CI). The primary outcome was postoperative complication (POC) rate, while secondary outcomes were severe POC and postoperative mortality. RESULTS: A total of 4 studies were included in the systematic review and meta-analysis, including 582 patients. A significant association between low skeletal muscle mass and POC was found (OR 1.45, 95% CI 1.04 to 2.03; p = 0.03), while no differences were found in terms operative time, estimated blood loss, length of hospital stay, and postoperative mortality (p > 0.05). CONCLUSIONS: Low skeletal muscle mass at diagnosis is a valid prognostic factor for POC development in colorectal and PM patients undergoing CRS. Prospective and larger studies are needed to better investigate the role of CT scan derived body composition and to understand how to implement this tool in clinical practice.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.034 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".