1370: EVALUATION OF PATIENT CHARACTERISTICS ASSOCIATED WITH PHENOBARBITAL USE FOR ACUTE ALCOHOL WITHDRAWAL
Bibliographic record
Abstract
Introduction: Phenobarbital (PHB) is used in patients with contraindications to benzodiazepines (BZD) or as an adjunct to BZD for those at risk for developing severe alcohol withdrawal syndrome (AWS). There is a lack of studies identifying the ideal population who may benefit from PHB use. While there is no guideline for PHB use at our institution, there has been a growing level of comfort with its use. The goal of this study was to identify shared characteristics among patients who received PHB for AWS to inform the development of a guideline. Methods: A retrospective chart review was conducted in patients who received BZD alone or BZD with PHB for AWS between July 2019 to June 2022. Key exclusion criteria included receiving BZD or PHB for indications besides AWS, concomitant BZD or opioid withdrawal, and intubation prior to admission. The primary objective was to compare characteristics of patients receiving PHB vs BZD alone. Key clinical endpoints included development of new AWS-related complications and incidence of treatment-related sedation requiring medication interruption. Results: Of 300 patients included, 100 (33%) received PHB and 200 (67%) received BZD alone. Patients receiving PHB were more likely to have prior admissions for AWS (49.0% vs 18.5%, p< 0.001). Additionally, patients in the PHB group had higher mean Clinical Institute Withdrawal Assessment of Alcohol Scale, Revised (CIWA-Ar) score (12.2 vs 8.5, p< 0.001) and higher mean Prediction of Alcohol Withdrawal Severity Scale (PAWSS) score (5.1 vs 3.7, p< 0.001) on presentation vs those receiving BZD alone. Patients in the PHB group also had higher incidence of history of alcohol withdrawal delirium (56.7% vs 30.5%, p< 0.001) and alcohol withdrawal seizures (41.9% vs 18.0%, p< 0.001) vs those in the BZD alone group. There was no significant difference in development of new AWS-related complications between the two groups (p=0.724), however patients receiving PHB had higher incidence of treatment-related sedation requiring medication interruption (10.0% vs 2.5%, p=0.019). Conclusions: Practitioners were more likely to use PHB in patients with higher PAWSS score on admission or those with risk factors for severe AWS including history of AWS-related delirium or seizures.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".