Serum <scp><i>Wisteria floribunda</i></scp> agglutinin‐positive Mac‐2‐binding protein evaluates liver function and predicts prognosis in liver cirrhosis
Bibliographic record
Abstract
OBJECTIVE Wisteria floribunda agglutinin‐positive Mac‐2‐binding protein (WFA+‐M2BP) is a novel glycobiomarker for evaluating liver fibrosis, but less is known about its role in liver cirrhosis (LC). This study aimed to investigate the utility of WFA+‐M2BP in evaluating liver function and predicting prognosis of cirrhotic patients. METHODS We retrospectively included 197 patients with LC between 2013 and 2016. Serum WFA+‐M2BP and various biochemical parameters were measured in all patients. With a median follow‐up of 23 months, liver‐related complications and deaths of 160 patients were recorded. The accuracy of WFA+‐M2BP in evaluating liver function, predicting decompensation and mortality were measured by the receiver operating characteristic (ROC) curve, logistic and Cox's regression analyses, respectively. RESULTS WFA+‐M2BP levels increased with elevated Child–Pugh classification, especially in patients with hepatitis B virus (HBV) infection. ROC analysis confirmed the high reliability of WFA+‐M2BP for the assessment of liver function using Child–Pugh classification. WFA+‐M2BP was also significantly positively correlated with the model for end‐stage liver disease (MELD) score. Multivariate logistic regression analysis indicated WFA+‐M2BP as an independent predictor of clinical decompensation for compensated patients (odds ratio 11.958, 95% confidence interval [CI] 1.876–76.226, P = 0.009), and multivariate Cox's regression analysis verified WFA+‐M2BP as an independent risk factor for liver‐related death in patients with HBV infection (hazards ratio 10.596, 95% CI 1.356–82.820, P = 0.024). CONCLUSION Serum WFA+‐M2BP is a reliable predictor of liver function and prognosis in LC and could be incorporated into clinical surveillance strategies for LC patients, especially those with HBV infection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".