Evaluation of a Novel Protocol for Assessment and Treatment of Alcohol Withdrawal Syndrome in Psychiatric Inpatients
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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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it