Practice Patterns in the Treatment of Patients With Severe Alcohol Withdrawal: A Multidisciplinary, Cross-Sectional Survey
Bibliographic record
Abstract
Purpose: To characterize physicians’ stated practices in the treatment of patients with severe acute alcohol withdrawal syndrome (sAAWS) and to use intravenous (IV) phenobarbital as an adjuvant treatment for sAAWS. Methods: A multidisciplinary, cross-sectional, self-administered survey at 2 large academic centers specializing in inner-city healthcare. Results: We analyzed 105 of 195 questionnaires (53.8% response rate). On average, clinicians managed 32 cases of AAWS over a 6-month period, of which 7 (21.9%) were severe. Haloperidol (Haldol; 40 [39%]), clonidine (Catapres; 31 [30%]), phenobarbital (Luminal, Tedral; 29 [27%]) and propofol (Diprivan; 29 [28%]) were the most commonly used adjuvant medications for sAAWS. Sixty-three (60%) of respondents did not use phenobarbital in practice. Of phenobarbital users, 23 (55%) respondents used it early in patients who were refractory to symptom-triggered benzodiazepine treatment. Others waited until patients experienced seizures (5 [10%]) or required intensive care unit admission (8 [18%]). Respondents who used phenobarbital preferred to use the IV versus oral form (66% vs 29%, P < .001). Most respondents, however, were unfamiliar with the pharmacokinetics, side effects, contraindications, and evidence supporting phenobarbital use for sAAWS. Although many respondents (64 [61%]) expressed discomfort using phenobarbital, 87 (83%) expressed comfort or neutrality with enrolling patients in a trial to evaluate IV phenobarbital in sAAWS. Conclusions: Considerable stated practice variation exists in how clinicians treat patients with sAAWS. Our findings support conduct of a pilot trial to evaluate IV phenobarbital as an adjuvant treatment to symptom-triggered benzodiazepines for sAAWS and have informed trial design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".