Lymphocyte‐C‐Reactive Protein Ratio: Impact on Prognosis of Patients Following Resection of Primary Liver Cancer
Bibliographic record
Abstract
OBJECTIVE: We sought to characterize the prognostic value of lymphocyte-C-reactive protein ratio (LCR) among patients undergoing liver resection (LR) for hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). METHODS: Patients who underwent curative-intent LR for HCC and ICC between 2000 and 2023 were identified from a multiinstitutional database. The prognostic value of nine different inflammatory markers were evaluated relative to short- (i.e., postoperative morbidity) and long-term (recurrence-free survival [RFS] and overall survival [OS]) outcomes. RESULTS: Among 715 patients, 499 (69.8%) and 216 (30.2%) individuals were included in the derivation and validation cohorts, respectively. Patients with advanced disease and poor tumor biology had lower median levels of LCR. An optimal LCR cutoff threshold of 6100 was identified in the derivation cohort. LCR demonstrated the highest accuracy to predict RFS and OS, with areas under the ROC curve of 0.724 and 0.716, respectively. After adjusting for relevant clinicodemographic factors, lower LCR remained associated with higher odds of postoperative complications (OR: 1.98 [95% CI: 1.27-3.10] and p = 0.003) and particularly, infectious complications (OR: 2.80 [95% CI: 1.57-5.01] and p < 0.001). A lower LCR was independently associated with worse RFS (HR: 2.43 [95% CI: 1.41-3.83] and p = 0.002) and OS (HR: 2.95 [95% CI: 2.10-4.16] and p < 0.001). The prognostic ability of LCR for short- and long-term outcomes performed well in an independent validation cohort. CONCLUSION: LCR was strongly associated with risk of postoperative morbidity as well as worse RFS and OS among patients undergoing LR for HCC and ICC. Preoperative LCR assessment can aid surgeons in the preoperative risk-stratification of patients undergoing surgery for primary liver cancer.
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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 | 0.001 | 0.003 |
| 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".