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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".