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Record W2142443678 · doi:10.4236/ojpsych.2014.41007

Disulfiram in the management of alcohol dependence: A comprehensive clinical review

2014· article· en· W2142443678 on OpenAlexaff
Gurvinder Kalra, Avinash De Sousa, Amresh Shrivastava

Bibliographic record

VenueOpen Journal of Psychiatry · 2014
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsDisulfiramAcamprosateNaltrexoneAlcohol dependenceMedicineAlcoholAdverse effectPharmacotherapyPharmacologyPsychiatryInternal medicineChemistryOpioid

Abstract

fetched live from OpenAlex

Disulfiram remains a viable option as a treatment for alcohol dependence and has been shown in recent studies to be successful in treating patients with alcohol dependence in a manner that is superior to both naltrexone and acamprosate. It is also useful in dual diagnosis patients and those with co-morbid cocaine and alcohol dependence. Although disulfiram’s mechanism of action in alcohol dependence was long thought to be its effects as a psychological deterrent, more recent studies have uncovered potential anticraving effects as well. Recent reviews exhort to the importance of supervised disulfiram therapy in highlighting many of the potential and unique benefits of disulfiram. The present article will review the major clinical trials of disulfiram spanning nearly 60 years. It also discusses the usage of disulfiram across diverse populations along with monitoring for compliance and various adverse effects that may be encountered. The paper also reviews certain studies on long acting disulfiram therapy, recent comparative trials of disulfiram and its use in alcohol dependence. The review concludes with the role of disulfiram in the present day and long-term pharmacotherapy of alcohol dependence along with future research needs in this area.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.228

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 head0.084
GPT teacher head0.429
Teacher spread0.345 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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