IDDF2021-ABS-0045 Efficacy of MAC-2 binding protein glycosylation isomer as fibrosis presence and severity biomarker in hepatitis: a systematic review
Bibliographic record
Abstract
Background In recent years, serum mac-2 binding Protein glycosylation isomer (M2BPGi) usefulness has been investigated in various liver diseases such as viral hepatitis and mortality in liver cirrhosis. However, its utilization in assessing the presence and severity of liver fibrosis in hepatitis patients remains unclear. Thus, this systematic review aims to investigate the efficacy of M2BPGi as a biomarker for assessing fibrosis presence and severity in hepatitis patients. Methods We searched data from PMC, PubMed, Google Scholar, Science Direct, and Scopus, on March 12th, 2021, using a combination of keywords associated with the M2BPGi biomarker in correlation with its diagnostic efficacy in detecting fibrosis and its stages, and their variations or synonyms. Publications included are limited to English manuscripts that were published in the past 10 years. We excluded patients with liver diseases other than hepatitis. Diagnostic efficacy is measured by M2BPGi sensitivity, specificity, positive predictive values (PPV), negative predictive value (NPV), and area under the receiver operating characteristic (AUROC) curve. All studies are reviewed and evaluated by all 7 authors. The quality of each included study was assessed using the Newcastle-Ottawa Scale (NOS). Results A total of 4 retrospective cohorts, 3 case-control, and 1 cross-sectional study were included consisting of 2116 patients. Based on NOS, all studies were good in quality. All studies showed that serum M2BPGi is a useful and reliable marker for non-invasive assessment and prediction of liver fibrosis in hepatitis patients. 4 studies also showed an association between serum M2BPGi level with fibrosis stages where higher M2BPGi level is correlated with more advanced stages of fibrosis. Conclusions Recent studies demonstrated that serum M2BPGi level is proven to be associated with the presence of fibrosis, and increases with higher fibrosis stage, making it a promising biomarker in assessing the presence of fibrosis and its severity in hepatitis patients. Further studies should be warranted to investigate M2BPGi usefulness in assessing fibrosis in other liver diseases as well.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.001 |
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".