A food product as a potential serious cause of liver injury
Bibliographic record
Abstract
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".