The Prognostic Value of Platelet Count in Patients With Hepatocellular Carcinoma
Bibliographic record
Abstract
Thrombocytopenia has been acknowledged to be a crucial risk factor for cirrhosis formation and hepatocarcinogenesis in chronic liver diseases. However, to date, the association between platelet count (PLT) and the prognosis of hepatocellular carcinoma (HCC) remains inconsistent and controversial. The aim of the present study was to determine whether PLT could be used as a useful predictor of survival in patients with HCC. We performed systematic review in online databases, including PubMed, EmBase, and Web of Science, from inception until 2014. Studies were included if a statistical relationship was investigated between PLT and survival for HCC, and hazard ratio (HR) and 95% confidence intervals (CIs) for overall survival (OS) or recurrence-free survival (RFS) were provided. The quality of each included study was assessed by Newcastle-Ottawa scale score. To synthesize these studies, a random-effects model or a fixed-effects model was applied as appropriate. Then, we calculated heterogeneity, performed sensitivity analysis, tested publication bias, and did subgrouped and meta-regression analysis. Finally, we identified 33 eligible articles (published from 1998 to 2014) involved 5545 patients by retrieval. A low level of preoperative PLT was found to be significantly associated with a poor survival of HCC. Irrespective of the therapy used, the pooled HRs for OS and RFS were 1.41 (95% CI, 1.14-1.75) and 1.44 (95% CI, 1.13-1.83), respectively. Specifically, in patients who underwent liver resection, the pooled HRs for OS and RFS were 1.67 (95% CI, 1.22-2.27) and 1.44 (95% CI, 1.04-1.99), respectively. Furthermore, patients with preoperative thrombocytopenia (PLT < 100 × 10⁹/L) had a worse OS (HR: 1.73, 95% CI, 1.29-2.32) and RFS (HR: 1.57, 95% CI, 1.31-1.87) in comparison with patients without thrombocytopenia. All our findings showed no significant changes due to the removal of any study or the use of an opposite-effects model, and there was no significant publication bias. The limitations of this meat-analysis were nonuniform cut-off values of PLT, high between-study heterogeneities, potential confounders, and a bias of publication year. A low preoperative PLT level results in an unfavorable outcome in HCC. PLT is a simple, inexpensive, and useful predictor of survival in patients with HCC.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".