Glycine Conjugation of Para-Aminobenzoic Acid (PABA): A Pilot Study of A Novel Prognostic Test in Acute Liver Failure in Children
Bibliographic record
Abstract
BACKGROUND: Fulminant hepatic failure (FHF) is associated with high mortality; few patients survive without liver transplantation. It is important to have a sensitive, specific early predictor of outcome to distinguish potential survivors (S) from nonsurvivors (NS). OBJECTIVE: Because we had previously shown that glycine conjugation of para-aminobenzoic acid (PABA) quantitatively reflects liver function in children with chronic liver disease, in this pilot study we wanted to determine whether the measurement of the glycine conjugates of PABA could distinguish S from NS in FHF in comparison with standard prognostic indices. METHODS: Twenty-four patients were studied: acute severe hepatitis (n = 7), subfulminant hepatic failure (n = 7), and FHF (n = 10). Assessment of King's College criteria, measurement of factor V and VII levels, PABA testing, and transjugular liver biopsies were performed in almost all patients within 48 hours of admission. Serum PABA and its glycine conjugates (para-aminohippurate (PAHA) and para-acetamidohippurate (PAAHA)) were measured thirty minutes after oral administration by high-pressure liquid chromatography. Poor prognostic categories as previously established in the literature were defined as factor V < 0.20U/ml, factor VII < 0.08 U/ml, % necrosis >70%, hippurate ratio = 0%, and PAHA = 0M. RESULTS: The measurement of PAHA was the best predictor of a poor outcome in patients with acute liver failure with a sensitivity of 92%, and negative predictive value (NPV) of 92% compared with a sensitivity of 54% and a NPV of 63% with King's College criteria. CONCLUSION: Measurement of serum PAHA is the best early prognostic marker of death in children who suffer from FHF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".