Correlation of postoperative splenic volume increase with prognosis of hepatocellular carcinoma after curative hepatectomy
Bibliographic record
Abstract
Background: Previous studies have reported a close connection between the spleen and hepatic tumours. We investigated the prognostic value of postoperative splenic volume increase (PSVI) in patients with hepatocellular carcinoma after curative hepatectomy. Methods: This was a retrospective study of adult patients with hepatocellular carcinoma who underwent hepatectomy between January 2007 and May 2013. We categorized patients into 2 groups according to the cut-off value of the receiver operating characteristic curve: group A (PSVI < 19.0%) and group B (PSVI ≥ 19.0%). We compared the clinicopathological data, overall survival and disease-free survival between the 2 groups. We performed univariate and multivariate analyses to identify factors associated with disease-free and overall survival. Results: There were 275 patients in group A and 196 patients in group B. The 1-, 3- and 5-year overall survival rates were 98.9%, 74.9% and 63.6%, respectively, for patients in group A, and 97.4%, 65.3% and 49.8%, respectively, for patients in group B (p = 0.004). The corresponding disease-free survival rates were 69.5%, 48.0% and 40.3%, and 58.1%, 36.5%, and 29.8% (p = 0.01). On multivariate analysis, PSVI was an independent predictor of overall (p = 0.01) and disease-free (p = 0.03) survival. Conclusion: Postoperative splenic volume increase correlates with poor prognosis of patients with hepatocellular carcinoma after curative hepatectomy.
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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 | 0.000 | 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".