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Record W3202362132 · doi:10.3138/canlivj-2021-0017

Late presentation of posterior reversible encephalopathy syndrome following liver transplantation in the setting of tacrolimus and cannabis use

2021· article· en· W3202362132 on OpenAlexaffvenue
Felix Zhou, Andreu F. Costa, Magnus McLeod

Bibliographic record

VenueCanadian Liver Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicNeurological Complications and Syndromes
Canadian institutionsQueen Elizabeth II Health Sciences CentreDalhousie University
Fundersnot available
KeywordsTacrolimusLiver transplantationPosterior reversible encephalopathy syndromeMedicineCannabisPresentation (obstetrics)EncephalopathyHepatic encephalopathyTransplantationIntensive care medicineInternal medicineSurgeryPsychiatryMagnetic resonance imagingRadiology

Abstract

fetched live from OpenAlex

A 45-year-old female presented to hospital with confusion and visual disturbances. She had undergone a liver transplant 3 years prior for cirrhosis secondary to primary biliary cholangitis. Computed tomography and magnetic resonance imaging of the brain showed features consistent with posterior reversible encephalopathy syndrome. Her medications included tacrolimus, sirolimus, and prednisone. She reported smoking 4 grams of cannabis per day. Following cessation of tacrolimus, the patient's encephalopathy and visual disturbances resolved. To our knowledge, this case represents the longest time elapsed from liver transplantation to the development of tacrolimus-associated posterior reversible encephalopathy syndrome in the literature. This case highlights the potential danger of cannabis use in transplant recipients who are on immunosuppressants such as tacrolimus. Clinicians should have a high index of suspicion for posterior reversible encephalopathy syndrome in post-transplant patients presenting with altered mental status, even years after liver transplantation, and be familiar with potential interactions between cannabis and immunosuppressants.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.023
GPT teacher head0.249
Teacher spread0.226 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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