Impact of osteopenia and osteosarcopenia on the outcomes after surgery of hepatobiliary-pancreatic cancers
Bibliographic record
Abstract
Objective The purpose of this study is to investigate potential associations between osteopenia, osteosarcopenia, and postoperative outcomes in patients with hepatobiliary-pancreatic cancer (HBPC). Methods Three online databases, including Embase, PubMed, and the Cochrane Library, were thoroughly searched for literature describing the relationship between osteopenia, osteosarcopenia, and outcomes of surgical treatment of HBPC patients from the start of each database to September 29, 2023. The Newcastle-Ottawa Scale was used to rate the quality of the studies. Results This analysis included a total of 16 articles with a combined patient cohort of 2,599 individuals. The results demonstrated that HBPC patients with osteopenia had significantly inferior OS (HR: 2.27, 95% CI: 1.70-3.03, p < 0.001) and RFS (HR: 1.96, 95% CI: 1.42-2.71, p < 0.001) compared to those without osteopenia. Subgroup analysis demonstrated that these findings were consistent across univariate and multivariate analyses, as well as hepatocellular carcinoma, biliary tract cancer, and pancreatic cancer. The risk of postoperative major complications was significantly higher in patients with osteopenia compared to those without osteopenia (OR: 1.66, 95% CI: 1.19-2.33, p < 0.001). Besides, we also found that the presence of osteosarcopenia in HBPC patients was significantly related to poorer OS (HR: 3.31, 95% CI: 2.00-5.48, p < 0.001) and PFS (HR: 2.50, 95% CI: 1.62-3.84, p < 0.001) in comparison to those without osteosarcopenia. Conclusion Preoperative osteopenia and osteosarcopenia can predict poorer OS and RFS with HBPC after surgery.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".