Baseline serum Mac-2 binding protein glycosylation isomer as a predictor of hepatocellular carcinoma in chronic hepatitis B patients: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: A minimally invasive tool to promptly predict hepatocellular carcinoma (HCC) in chronic hepatitis B (CHB) is currently needed. In this study, we aimed via a meta-analysis to identify the serum Mac-2 binding protein glycosylation isomer (M2BPGi) as a novel glycoprotein-based liver fibrosis marker for predicting HCC in CHB patients. Methods: We conducted a systematic search on PubMed, Scopus, ProQuest, Wiley Online Library, and CINAHL Plus (via EBSCOhost). The articles were screened based on several eligibility criteria and were further assessed for study qualities using the Newcastle-Ottawa Scale. The outcomes were presented as standard mean difference (SMD), hazard ratio (HR), and predictive accuracy parameters of a baseline cutoff index (COI) for serum M2BPGi. Results: Fourteen studies involving 5918 CHB patients were included in this systematic review and meta-analysis. Baseline COI serum M2BPGi was significantly higher in CHB patients who developed HCC than in those who did not (SMD 1.32, 95% confidence interval [CI] 0.91-1.72). A significant HCC risk prediction was also observed (multivariate HR 1.18, 95%CI 1.05-1.32). Baseline COI serum M2BPGi could predict HCC with a pooled sensitivity of 74% (95%CI 50-89%), specificity of 80% (95%CI 65-90%), and area under the summary receiver operating characteristic curve of 0.84 (95%CI 0.81-0.87). Conclusion: High baseline COI serum M2BPGi may predict the development of HCC in CHB patients with moderate-to-high accuracy.
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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) | 0.016 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".