Clinical implications of serum <scp><i>Wisteria floribunda</i></scp> agglutinin‐positive Mac‐2‐binding protein in treatment‐naïve chronic hepatitis B
Bibliographic record
Abstract
Aim To examine the relationship between serum Wisteria floribunda agglutinin‐positive Mac‐2‐binding protein (WFA+‐M2BP) levels and liver histological findings for patients with treatment naïve chronic hepatitis B (CHB). Methods A total of 189 treatment naïve‐CHB patients were analyzed. We examined the effect of pretreatment serum WFA+‐M2BP levels on histological findings compared with other laboratory markers, including aspartate aminotransferase (AST) to platelet ratio index, Fibrosis‐4 index, platelet count, AST to alanine aminotransferase (ALT) ratio, and hyaluronic acid as liver fibrosis markers, and AST value, ALT value, and serum interferon‐γ‐inducible protein‐10 level as liver inflammation markers. Results The WFA+‐M2BP value ranged from 0.3 cut‐off index (COI) to 12.9 COI (median value, 1.2 COI). The degree of liver fibrosis was significantly stratified according to WFA+‐M2BP level in each group except for groups F2 and F3 and the degree of liver inflammation activity was significantly stratified according to WFA+‐M2BP level in each group. For predicting F4, WFA+‐M2BP level yielded the highest area under the receiver operating characteristic curve (AUROC) with a level of 0.87 and for predicting advanced liver fibrosis (≥F3) and significant liver fibrosis (≥F2), WFA+‐M2BP level yielded the second highest AUROCs (both, 0.77) among six fibrotic markers. For predicting severe (A3) or significant liver inflammation activity (≥A2), AUROCs of WFA+‐M2BP level were 0.78 and 0.76. Conclusion The WFA+‐M2BP level can be a useful marker for assessing liver histological findings in patients with treatment‐naïve CHB, although it has several limitations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".