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Record W2347170464 · doi:10.1159/000444012

Resected Hepatocellular Carcinoma in a Patient with Crohn’s Disease on Azathioprine

2016· article· en· W2347170464 on OpenAlexaff
Valérie Heron, Kyle J. Fortinsky, Gillian Spiegle, Nir Hilzenrat, Andrew Szilagyi

Bibliographic record

VenueCase Reports in Gastroenterology · 2016
Typearticle
Languageen
FieldMedicine
TopicAcute Lymphoblastic Leukemia research
Canadian institutionsUniversity of TorontoMcGill University
Fundersnot available
KeywordsMedicineHepatocellular carcinomaAzathioprineCirrhosisGastroenterologyBiopsyInternal medicineLiver biopsyLiver diseaseCarcinomaCrohn's diseaseDisease

Abstract

fetched live from OpenAlex

Hepatocellular carcinoma rarely occurs in patients without underlying cirrhosis or liver disease. While inflammatory bowel disease has been linked to certain forms of liver disease, hepatocellular carcinoma is exceedingly rare in these patients. We report the twelfth case of hepatocellular carcinoma in a patient with Crohn's disease. The patient is a 61-year-old with longstanding Crohn's disease who was treated with azathioprine and was found to have elevated liver enzymes and a new 3-cm liver mass on ultrasound. A complete workup for underlying liver disease was unremarkable and liver biopsy revealed hepatocellular carcinoma. The patient underwent a hepatic resection, and there is no evidence of recurrence at the 11-month follow-up. The resection specimen showed no evidence of cancer despite the initial biopsy revealing hepatocellular carcinoma. This case represents the third biopsy-proven complete spontaneous regression of hepatocellular carcinoma. Although large studies have failed to show a definite link between azathioprine and hepatocellular carcinoma, the relationship remains concerning given the multiple case reports suggesting a possible association. Clinicians should exercise a high degree of suspicion in patients with Crohn's disease who present with elevated liver enzymes, especially those on azathioprine therapy.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.249
Teacher spread0.236 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2016
Admission routes1
Has abstractyes

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