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Record W4386726580 · doi:10.1080/15563650.2023.2256469

A food product as a potential serious cause of liver injury

2023· article· en· W4386726580 on OpenAlexaff
Stephanie E. Chan, Christopher A. Smith

Bibliographic record

VenueClinical Toxicology · 2023
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicDrug-Induced Hepatotoxicity and Protection
Canadian institutionsKingston General HospitalQueen's University
Fundersnot available
KeywordsLiver injuryMedicineDrugMedical prescriptionEpigastric painFood and drug administrationIntensive care medicineInternal medicinePharmacology

Abstract

fetched live from OpenAlex

INTRODUCTION: Drug-induced liver injury can be challenging to diagnose, as it can develop following the use of many prescription and nonprescription medications, herbals, and dietary supplements. Food products may not be routinely considered as a potential cause of hepatotoxicity. We describe the clinical features of two cases of acute liver injury following consumption of a smoothie product. CASE PRESENTATIONS: Two patients independently presented to the hospital with epigastric pain and acute liver injury. Both patients had consumed a new smoothie product in the same month that they presented to the hospital, with a recurrence of acute liver injury with further consumption. A diagnosis of drug-induced liver injury was established after the evaluation excluded other causes of liver injury. It was thought that a natural ingredient in the smoothie, tara flour, was the cause of hepatotoxicity based on prior news reports. Both patients stopped drinking the smoothie product with subsequent normalization of liver enzyme activities and no further recurrence of epigastric pain. CONCLUSION: The diagnosis of drug-induced liver injury largely relies on a compatible history and exclusion of other causes of liver injury. We demonstrate the importance of considering new food products in the differential diagnosis of acute liver injury.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.571
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.273
GPT teacher head0.512
Teacher spread0.239 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations5
Published2023
Admission routes1
Has abstractyes

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