Diagnostic accuracy of serum biochemical fibrosis markers in children with chronic hepatitis B evaluated by receiver operating characteristics analysis
Bibliographic record
Abstract
AIM: To investigate the diagnostic accuracy of potent serum biochemical fibrosis markers in children with chronic hepatitis B evaluated by receiver operating characteristics (ROC) analysis.METHODS: We determined the serum level of apolipoprotein A-I (APO A-I), haptoglobin (HPT) and a-2macroglobulin (A2M) with an automatic nephelometer in 63 children (age range 4-17 years, mean 10 years)with biopsy-verified chronic HBeAg-positive hepatitis B.Fibrosis stage and inflammation grade were assessed in a blinded fashion according to Batts and Ludwig. We defined mild liver fibrosis as a score ≤2 and advanced fibrosis as a score equal to 3. ROC analysis was used to calculate the power of the assays to detect advanced liver fibrosis (AccuROC, Canada).RESULTS: Serum concentrations of APO A-I, HPT and A2M were not significantly different in patients with chronic hepatitis B compared to controls. However, APO A-I level of 1.19 ng/L had a sensitivity of 85.7% and a specificity of 60.7% (AUC = 0.7117, P = 0.035) to predict advanced fibrosis. All other serum biochemical markers and their combination did not allow a useful prediction.None of these markers was a good predictor of histologic inflammation.CONCLUSION: Apolipoprotein A-I may be a suitable serum marker to predict advanced liver fibrosis in children with chronic hepatitis B.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".