Body composition as a prognostic factor in cholangiocarcinoma: a meta-analysis
Bibliographic record
Abstract
This investigation seeks to scrutinize the relationships between body composition metrics and the clinical outcomes observed in patients with cholangiocarcinoma (CCA). A comprehensive exploration was conducted across three prominent online databases: Embase, PubMed, and the Cochrane Library. This endeavor spanned the entirety of each database up to the cutoff date of September 29, 2023. To evaluate the quality of the included studies, the Newcastle-Ottawa scale was employed. This comprehensive analysis included a total of 26 articles with a combined patient cohort of 4398 individuals. The results demonstrated that CCA patients with low skeletal muscle index (SMI) had significantly inferior OS (HR: 1.93, p < 0.001) and RFS (HR: 2.02, p < 0.001), as well as a higher incidence of postoperative complications (OR: 1.69, 95% CI: 1.20-2.38, p < 0.001) compared to those with high SMI. The presence of sarcopenia in CCA patients was significantly related to poorer OS (HR: 1.96, p < 0.001) and RFS (HR: 2.05, p < 0.001), and a higher rate of postoperative complications (OR: 1.39, p = 0.049) in comparison to those without sarcopenia. Moreover, lower psoas muscle index (PMI) and myosteatosis were associated with shorter OS (PMI, HR: 1.56, p < 0.001; myosteatosis, HR: 1.49, p = 0.001) and RFS (PMI, HR: 2.16, p < 0.001; myosteatosis, HR: 1.35, p = 0.023). Our findings highlight incorporating body composition screening into clinical practice can help develop treatment strategies and optimize perioperative care, potentially improving patient 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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.036 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 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".