Acute liver injury and acute liver failure from mushroom poisoning in <scp>N</scp>orth <scp>A</scp>merica
Bibliographic record
Abstract
BACKGROUND & AIMS: Published estimates of survival associated with mushroom (amatoxin)-induced acute liver failure (ALF) and injury (ALI) with and without liver transplant (LT) are highly variable. We aimed to determine the 21-day survival associated with amatoxin-induced ALI (A-ALI) and ALF (A-ALF) and review use of targeted therapies. METHODS: Cohort study of all A-ALI/A-ALF patients enrolled in the US ALFSG registry between 01/1998 and 12/2014. RESULTS: Of the 2224 subjects in the registry, 18 (0.8%) had A-ALF (n = 13) or A-ALI (n = 5). At admission, ALF patients had higher lactate levels (5.2 vs. 2.2 mm, P = 0.06) compared to ALI patients, but INR (2.8 vs. 2.2), bilirubin (87 vs. 26 μm) and MELD scores (28 vs. 24) were similar (P > 0.2 for all). Of the 13 patients with ALF, six survived without LT (46%), five survived with LT (39%) and two died without LT (15%). Of the five patients with ALI, four (80%) recovered and one (20%) survived post-LT. Comparing those who died/received LT (non-spontaneous survivors [NSS]) with spontaneous survivors (SS), N-acetylcysteine was used in nearly all patients (NSS 88% vs. SS 80%); whereas, silibinin (25% vs. 50%), penicillin (50% vs. 25%) and nasobiliary drainage (0 vs. 10%) were used less frequently (P > 0.15 for all therapies). CONCLUSION: Patients with mushroom poisoning with ALI have favourable survival, while around half of those presenting with ALF may eventually require LT. Further study is needed to define optimal management (including the use of targeted therapies) to improve survival, particularly in the absence of LT.
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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 | 0.001 |
| 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) | 0.002 | 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".