Clinical heterogeneity in binary EAD definition and proposal of new EAD classification after liver transplantation: a multicenter study
Bibliographic record
Abstract
BACKGROUNDAIMS: Current binary definition of early allograft dysfunction (EAD) was not sufficiently accurate for discriminating clinical outcomes after liver transplantation (LT). We investigated the clinical heterogeneity among EAD sub-criteria and explored the necessity of dividing EAD into different stages to grade the severity of graft dysfunction. METHODS: 1242 LT patients from 5 centers were included. EAD patients were divided as i) EAD-type-A: only AST/ALT criteria; ii) EAD-type-B: bilirubin or INR criteria; iii) EAD-type-C: meeting two or three EAD sub-criteria. Peri-operative clinical complications and survival outcomes were compared. RESULTS: Three-month early graft failure (EAF) from non-EAD to EAD-type-C were 1.6%, 3.5%, 12.8% and 29.6%. EAD-type-B and EAD-type-C were significantly associated with higher rates of AKI, RRT, in-hospital death, longer hospital stay, ICU stay, ventilator support time, and inferior one-year survival outcomes(P<0.001); However, there were no statistical differences between EAD-type-A and non-EAD (P>0.05). New EAD classification with three stages was proposed to grade EAD severity: a)EAD-stage-I: only ALT/AST≥2000 U/L within POD7; b)EAD-stage-II: only bilirubin 10-30 mg/dL or INR≥1.6 on POD7; c)EAD-stage-III: bilirubin≥30 mg/dL; both bilirubin≥10 mg/dL and INR≥1.6 on POD7. Clinical outcomes and survival rates deteriorated following EAD stages. New EAD classification had an excellent discrimination (AUROC = 0.84, CI 0.81-0.86) in determining EAF, superior to binary EAD definition (AUROC = 0.73, CI 0.70-0.77) and MEAF (AUROC = 0.76, CI 0.73-0.79) (P<0.001), while similar to L-GrAFT-7(AUROC = 0.87, CI = 0.84-0.90 P>0.05). Consistent with findings in derivation cohort, external validation confirmed its excellent discrimination of graft dysfunction and 3-month EAF. CONCLUSIONS: Different EAD sub-criteria had significantly different clinical outcomes. EAD definition should be further reclassified with different severities. New EAD classification with 3 stages could be serve as an effective tool to accurately grade the severity of EAD and identify patients in high risk of early graft failure.
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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 | 0.000 |
| 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) | 0.000 | 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".