Evaluation of a Novel Protocol for Assessment and Treatment of Alcohol Withdrawal Syndrome in Psychiatric Inpatients
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Despite the high incidence of alcohol withdrawal syndrome (AWS) in psychiatric inpatients, standardized methods for assessing and treating AWS have been studied only once before in this population. We evaluated a novel AWS assessment and treatment protocol designed for psychiatric inpatients. METHODS: This retrospective cohort study evaluated outcomes before and after implementation of the protocol. We collected consecutive data on patients (N = 138) admitted to inpatient psychiatric units at a single center. Participants were patients admitted for nonsubstance-related psychiatric reasons, who were also at risk for developing AWS. Those who developed AWS were treated with either (a) treatment as usual (TAU) or (b) a novel standardized protocol. The primary outcome was duration of benzodiazepine treatment for symptoms of alcohol withdrawal. Secondary outcomes included cumulative benzodiazepine dose administered, treatment duration, and incidence of complications. RESULTS: Of 138 participants, 83 received TAU and 55 were assessed and treated with the novel protocol. Median duration of benzodiazepine treatment following protocol implementation was 19.7 hours (interquartile range [IQR], 0-46) prior to implementation (TAU) and 0 hours (IQR, 0-15) following protocol implementation (protocol group) (P < .0001). Median benzodiazepine dose (in diazepam equivalents) administered to participants was 30 mg (IQR, 0-65) for TAU and 5 mg (IQR, 0-30) for the protocol group (P < .001). Adverse events before and after implementation occurred in 4.8% and 0%, respectively (P = .15). CONCLUSION AND SCIENTIFIC SIGNIFICANCE: This study provides preliminary evidence for the efficacy and safety of a novel standardized AWS protocol for psychiatric inpatients. This is the first known study assessing an AWS assessment and treatment protocol designed for psychiatric inpatients. (Am J Addict 2020;29:500-507).
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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 | 0.064 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".