<scp>SAEM GRACE</scp>: Phenobarbital for alcohol withdrawal management in the emergency department: A systematic review of direct evidence
Bibliographic record
Abstract
OBJECTIVES: Alcohol withdrawal syndrome (AWS) is a commonly presenting condition in the emergency department (ED) and can have severe complications, including mortality. Benzodiazepines are first-line medications for treating AWS but may be unavailable or insufficient. This systematic review evaluates the direct evidence assessing the utility of phenobarbital for treating AWS in the ED. METHODS: A systematic search was conducted and designed according to the patient-intervention-comparator-outcome (PICO) question: (P) adults (≥18 years old) presenting to the ED with alcohol withdrawal; (I) phenobarbital (including adjunctive); (C) benzodiazepines or no intervention; and (O) AWS complications, admission to a monitored setting, control of symptoms, adverse effects, and adjunctive medications. Two reviewers independently assessed articles for inclusion and conducted risk of bias assessments for included studies. RESULTS: From 70 potentially relevant articles, seven studies met inclusion criteria: three retrospective cohort studies, two retrospective chart reviews, and two randomized controlled trials (RCTs), one examining phenobarbital monotherapy and one examining adjunctive phenobarbital. Across the retrospective cohort studies, treatment of AWS with phenobarbital resulted in lower odds of a subsequent ED visit. The retrospective chart reviews indicated that phenobarbital was associated with higher discharge rate compared to benzodiazepine-only treatments. For the two RCTs, phenobarbital did not differ significantly from benzodiazepine for most outcomes, although concomitant treatment with phenobarbital was associated with lower benzodiazepine use and intensive care unit admission. The heterogeneous designs and small number of studies prevented quantitative synthesis. CONCLUSIONS: Relatively few studies provide direct evidence on the utility of phenobarbital for AWS in the ED, but the evidence that exists generally suggests that it is a reasonable and appropriate approach. Additional RCTs and other methodologically rigorous investigations are needed for more definitive direct evidence.
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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".