MétaCan
Menu
Back to cohort
Record W4323360343 · doi:10.7759/cureus.35852

Use of Oral N-Acetylcysteine (NAC) in Non-Acetaminophen-Induced Acute Hepatic Failure

2023· article· en· W4323360343 on OpenAlexaff
Saleem Sharieff, Asim Idrees, Wajid Rafai, Syed Uzair S Bukhari

Bibliographic record

VenueCureus · 2023
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsGrand River Hospital
Fundersnot available
KeywordsMedicineAcetylcysteineAcetaminophenLiver failureacetaminophen overdoseGastroenterologyInternal medicinePharmacologyAntioxidantBiochemistry

Abstract

fetched live from OpenAlex

BACKGROUND: Acute liver failure (ALF) is a syndrome rather than a specific disease with several possible causes, and viral hepatitis is a major cause. The objective of the study was to assess the benefit of N-acetylcysteine (NAC) in non-acetaminophen-induced acute liver failure (NAI-ALF). METHODS: A total of six patients with a diagnosis of acute liver failure (ALF) were included in the study. All six patients received oral NAC for 72 hrs. The parameters evaluated were demographic, clinical, biochemical, outcome, and length of ICU and hospital stay. The primary outcome was a reduction in mortality with the use of NAC in NAI-ALF. The secondary outcomes were to evaluate the safety of NAC and assess factors predicting mortality. RESULTS: All patients improved and returned to normal or near-normal liver function with the use of NAC. No side effects were noted, and the use of NAC was associated with a shorter hospital stay. CONCLUSION: In patients with non-acetaminophen-related acute liver failure, N-acetyl-L-cysteine (NAC) significantly improves overall survival and also decreases the length of hospital stay.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.211
GPT teacher head0.430
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCureusSame topicDrug-Induced Hepatotoxicity and ProtectionFrench-language works237,207