Disulfiram in the management of alcohol dependence: A comprehensive clinical review
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".