Valproic Acid Management of Acute Alcohol Withdrawal
Bibliographic record
Abstract
OBJECTIVE: To review the clinical evidence to determine the efficacy and safety of valproic acid in the management of alcohol withdrawal syndrome (AWS). DATA SOURCES: MEDLINE (1966-February 2006), EMBASE (1980-February 2006), and PubMed (1966-February 2006) searches identified pertinent studies that were conducted in humans and published in English. Key words used for identification of articles included valproic acid, ethanol, alcohol, alcoholism, alcohol withdrawal delirium, alcohol withdrawal seizures, and substance withdrawal syndrome. References of identified articles were manually searched. STUDY SELECTION AND DATA EXTRACTION: All controlled clinical trials that evaluated the use of valproic acid for the management of AWS in humans were included. DATA SYNTHESIS: Comparisons were made among various regimens of valproic acid and traditional therapy with benzodiazepine or nonbenzodiazepine agents. Only 2 of 6 trials reported a statistically significant difference in favor of valproic acid on endpoints of AWS. However, these differences were of marginal clinical significance. The number of patients included in these studies did not allow for adequate evaluation of safety. CONCLUSIONS: The existing limited efficacy and safety data suggest that valproic acid should not replace conventional therapy or be used as adjunct therapy for management of 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".