Refined hepatic grading system in chronic graft‐versus‐host disease improves prognostic risk stratification of long‐term outcomes
Bibliographic record
Abstract
OBJECTIVES: Hepatic grading systems for categorizing severity in chronic graft-versus-host disease (cGvHD) were determined arbitrarily, leading us to initiate the present study to provide objective evidence for the determination of optimal cutoff values and devise a hepatic grading system to predict prognosis. METHODS: Of 842 patients who received allogeneic hematopoietic stem transplant (HCT), 336 patients diagnosed with cGvHD were evaluated for overall survival (OS) and non-relapse mortality (NRM) after cGVHD development. Multiple statistical parameters were evaluated to define optimal cutoff values of liver profile, including negative predictive value (NPV), positive predictive value (PPV), accuracy, and p-values as measures of risk stratification power. RESULTS: We found that alkaline phosphatase (ALP) ≥ 146 IU/L (NPV: 83.4%; PPV: 32.8%; accuracy: 52.7%) and bilirubin ≥ 14 µmol/L (NPV: 81.8%; PPV: 39.4%; accuracy 68.1%) significantly correlated with OS. We developed a refined hepatic cGvHD grading score (RHS), stratifying patients into a low-RHS group with RHS score 0, OS at 3 years (n = 162) to 80.5%, compared to high-RHS group with score 1-2 (n = 172) 62.7%. Regarding NRM, score 0 segregated NRM at 3 years to 11.9%, compared with score 1-2 19.6%, P = .1. CONCLUSIONS: Refined hepatic score is promising for stratifying patients with cGVHD and liver involvement according to long-term outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".