Immunophenotype Predicts Outcome in Pediatric Acute Liver Failure
Bibliographic record
Abstract
OBJECTIVES: We sought to determine whether markers of T-cell immune activation, including soluble interleukin 2 receptor alpha (sIL2Rα) levels predict outcome in pediatric acute liver failure and may target potential candidates for immunomodulatory therapy. METHODS: We analyzed markers of immune activation in 77 patients with pediatric acute liver failure enrolled in a multinational, multicenter study. The outcomes were survival with native liver, liver transplantation (LT), and death without transplantation within 21 days after enrollment. RESULTS: Adjusting for multiple comparisons, only normalized serum sIL2Rα level differed significantly among the 3 outcomes, and was significantly higher in patients who died (P=0.02) or underwent LT (P=0.01) compared with those who survived with their native liver. The 37 patients with normal sIL2Rα levels all lived, 30 with their native liver. Of the 15 subjects with markedly high sIL2Rα (≥5000 IU/mL), 5 survived with their native liver, 2 died, and 8 underwent LT. CONCLUSIONS: Evidence of immune activation is present in some patients who die or undergo LT. Patients with higher sIL2Rα levels were more likely to die or undergo LT within 21 days than those with lower levels. Identifying a subset of patients at risk for poor outcome may form the foundation for targeted clinical trials with immunomodulatory drugs.
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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 | 0.001 | 0.001 |
| 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".