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Record W2167828061 · doi:10.1111/dar.12289

Pharmacotherapy for alcohol addiction in a patient with alcoholic cirrhosis and massive upper gastrointestinal bleed: A case study

2015· article· en· W2167828061 on OpenAlexafffund
Samantha Young, Evan Wood, Keith Ahamad

Bibliographic record

VenueDrug and Alcohol Review · 2015
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
FundersNational Institutes of HealthNational Institute on Drug AbuseCanada Research Chairs
KeywordsAlcohol use disorderMedicineContext (archaeology)PsychiatryAlcoholAlcohol dependenceAlcoholic hepatitisPharmacotherapyAlcoholic liver diseaseBleedIntensive care medicineAddictionCirrhosisInternal medicineSurgery

Abstract

fetched live from OpenAlex

Abstract Alcohol use causes a substantial burden of morbidity and mortality worldwide. The pharmacologic treatment of alcohol dependence has been increasingly studied and proven to improve outcomes in individuals with alcohol use disorder. However, the treatment of alcohol use disorder is often challenging in the context of patients with hepatic impairment as many medications to treat alcohol use disorder are hepatically metabolised or may cause liver toxicity in some instances. We present a case history of an individual with significant medical complications from alcohol use disorder and explore the dilemma faced in prescribing pharmacologic treatment of alcohol use disorder in patients with significant liver dysfunction. [Young S, Wood E, Ahamad K. Pharmacotherapy for alcohol addiction in a patient with alcoholic cirrhosis and massive upper gastrointestinal bleed: A case study. Drug Alcohol Rev 2016;35:236–239]

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.110
GPT teacher head0.407
Teacher spread0.297 · 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 designOther design
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
Published2015
Admission routes2
Has abstractyes

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