Changes in Inflammatory Markers Predict the Prognosis of Resected Hepatocellular Carcinoma with Child–Pugh A
Bibliographic record
Abstract
(1) Background: The reasons for changes in the inflammatory markers of patients with surgically resected hepatocellular carcinoma are unclear. We aimed to investigate the association of an inflammatory status with the prognosis of patients with hepatocellular carcinoma, who underwent surgical resection. (2) Methods: We retrospectively enrolled 91 patients with Child A hepatocellular carcinoma, who had received surgical resection, to explore the influence of preoperative inflammatory markers and postoperative changes on the prognosis. (3) Results: The platelet-to-lymphocyte ratio (PLR) and its alteration were independent prognostic factors. Patients with a low PLR had a significantly better recurrence-free survival (RFS) than those with a high PLR (1-year RFS of 88.5% versus 50.0%; 3-year RFS of 62.1% versus 25.0%, p = 0.038). The patients with a low PLR showed a significantly better overall survival (OS) than those with a high PLR (1-year OS of 98.9% versus 75.0%; 3-year OS of 78.2% versus 25.0%, p = 0.005). The patients whose PLR had increased at 6 months after operation showed a worse OS than patients whose PLR had decreased (1-year OS of 96.3% versus 98.4%; 3-year OS of 63.0% versus 79.7%, p = 0.048). However, neither the neutrophil-to-lymphocyte ratio nor Onodera’s prognostic nutritional index had any prognostic significance. (4) Conclusions: The PLR and its alteration are significant prognostic factors for the RFS and OS of patients with Child A hepatocellular carcinoma who had received curative surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".