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Record W4387999529 · doi:10.1016/j.radcr.2023.10.001

Metronidazole-induced encephalopathy in a patient with cirrhosis

2023· article· en· W4387999529 on OpenAlexaff
Xinyu Ji, Ke Xuan Li, Leonardo Furtado Freitas, Philippe Huot

Bibliographic record

VenueRadiology Case Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicInfectious Encephalopathies and Encephalitis
Canadian institutionsMcGill University Health CentreMontreal Neurological Institute and HospitalMcGill University
FundersSunovion
KeywordsMedicineSpleniumMetronidazoleEncephalopathyDeliriumMagnetic resonance imagingToxic encephalopathyPathologyRadiologyInternal medicineIntensive care medicineAntibioticsWhite matter

Abstract

fetched live from OpenAlex

Metronidazole is a commonly used antibiotic with anaerobic bacterial, protozoal, and microaerophilic bacterial coverage. Encephalopathy and peripheral neurotoxicity are rare but known adverse events with prolonged metronidazole use, which can be difficult to distinguish from other causes of delirium in acutely ill patients. Definitive diagnosis can be made by brain magnetic resonance imaging (MRI), which often reveals symmetric bilateral hypersignal demyelination lesions typically involving the dentate nuclei, splenium of the corpus collosum, midbrain, dorsal medulla, and pons. This case report describes a 72-year-old male presenting with altered mental status and neurological deficits after prolonged metronidazole use for bacteremia with spondylodiscitis, with full clinical and neuroradiological resolution upon appropriate diagnosis and drug cessation. Neuroradiologists play an indispensable role in recognizing this rare and poorly understood manifestation.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.013
GPT teacher head0.256
Teacher spread0.243 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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