Data-Driven Modeling for Precision Medicine in Pediatric Acute Liver Failure
Bibliographic record
Abstract
Absence of early outcome biomarkers for Pediatric Acute Liver Failure (PALF) hinders medical and liver transplant decisions. We sought to define dynamic interactions among circulating inflammatory mediators to gain insights into PALF outcome sub-groups. Serum samples from 101 participants in the PALF study, collected over the first 7 days following enrollment, were assayed for 27 inflammatory mediators. Outcomes (Spontaneous survivors [S, n=61], Non-survivors [NS, n=12], and liver transplant patients [LTx, n=28]) were assessed at 21 days post-enrollment. Dynamic interrelations among mediators were defined using data-driven algorithms. Dynamic Bayesian Network inference identified a common network motif with HMGB1 as a central node in all patient sub-groups. The networks in S and LTx were similar, and differed from NS. Dynamic Network Analysis suggested similar dynamic connectivity in S and LTx, but a more highly-interconnected network in NS that increased with time. A Dynamic Robustness Index calculated to quantify how inflammatory network connectivity changes as a function of correlation stringency differentiated all three patient sub-groups. Our results suggest that increasing inflammatory network connectivity is associated with non-survival in PALF, and may ultimately lead to better patient outcome stratification.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".