Anticonvulsants as Monotherapy Or Adjuncts to Treat Alcohol Withdrawal: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: This systematic review evaluates current literature on anticonvulsants to treat alcohol withdrawal symptoms (AWS). METHODS: We performed a literature search of PubMed, MEDLINE, PsycINFO, EMBASE, and Cochrane collaboration databases through September 30, 2016. The search was not restricted by patients' age. Articles published in English or with official English translations were included. RESULTS: We found 16 double-blind randomized controlled trials (RCTs) that evaluated the use of anticonvulsants as treatment of AWS. Available data indicates that anticonvulsants are as effective as sedatives/hypnotics in treating mild or moderate AWS. Two studies evaluated the use of anticonvulsants as adjuncts. Combining anticonvulsants with sedatives decreases the quantity of sedatives required and AWS may resolve quicker. There is some data that anticonvulsants can be used to treat AWS as monotherapy. Fourteen of these studies assessed adverse effects of these medications; 13 studies identified minor adverse effects and one found the adverse effects to be intolerable. CONCLUSIONS: Available evidence indicates that anticonvulsants have good efficacy as monotherapy and as adjuncts with sedatives/hypnotics in treating mild to moderate AWS.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".