Stratified alpha-fetoprotein pattern accurately predicts mortality in patients with acute-on-chronic hepatitis B liver failure
Bibliographic record
Abstract
BACKGROUND: Alpha-fetoprotein (AFP) has been shown to predict the prognosis of liver disease in several studies. This study aimed to evaluate the prognostic value of stratified AFP in patients with acute-on-chronic hepatitis B liver failure (ACHBLF). METHODS: A total of 192 patients were included and AFP were categorized into quartiles. The prognostic value was determined for overall survival (OS) and assessed by Kaplan-Meier analysis. Univariate and multivariate Cox proportional hazard regression analyses studied the association of all independent parameters with disease prognosis. RESULTS: The optimal cut-off points of AFP were: (Q1) 252.3-4800.0 ng/ml, (Q2) 76.0-252.2 ng/ml, (Q3) 18.6-75.9 ng/ml, and (Q4) 0.7-18.5 ng/ml. Based on the Kaplan-Meier analysis of the OS, each AFP quartile revealed a progressively worse OS and apparent separation (log-rank P = 0.006). The second-highest quartiles of AFP (Q2) always demonstrated an extremely favorable short-term survival. Combining the lowest AFP quartiles with a serum sodium < 131mmol/L or an INR ≥ 3.3 showed a poor outcome (90-days survival of 25.0% and 11.9% respectively). CONCLUSIONS: Stratified AFP could strengthen the predictive power for short-term survival of patients with ACHBLF. Combining AFP quartiles with low serum sodium and high INR may better predict poor outcome in ACHBLF patients.
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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 | 0.002 |
| 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".