Prognostic utility of preoperative inflammatory markers in patients with intrahepatic cholangiocarcinoma after hepatic resection: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: The prognostic value of preoperative systemic inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR), remains controversial in patients with intrahepatic cholangiocarcinoma (ICC). Therefore, this meta-analysis aimed to investigate the prognostic value of preoperative NLR, PLR, and LMR in patients with ICC who underwent hepatic resection. METHODS: We conducted a comprehensive search of four electronic databases. Two researchers assessed the quality of the available data using the Newcastle-Ottawa Scale. We selected overall survival (OS) as the primary outcome and recurrence-free survival (RFS) and disease-free survival (DFS) as secondary outcomes. Hazard ratios (HRs) and 95% confidence intervals (CIs) were merged to evaluate the associations between inflammatory markers and ICC patient prognosis. RESULTS: Fifteen studies (18 cohorts) with 4123 cases were included in this meta-analysis. The results revealed that a high preoperative NLR was associated with short OS and RFS (HR = 1.04, 95% CI: 1.01-1.07, and HR = 1.29, 95% CI: 1.04-1.60, respectively) in patients with ICC. However, the association between PLR or LMR and ICC prognosis was not statistically significant. In addition, the publication bias and sensitivity analyses demonstrated that the results were reliable and stable. CONCLUSION: Our meta-analysis revealed that preoperative NLR may be a useful prognostic predictor for patients with ICC.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.026 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".