Assessment of the Treatment for Alcohol Withdrawal Syndrome With and Without Dexmedetomidine: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: Alcohol withdrawal syndrome (AWS) carries a high risk of morbidity and mortality that often requires critical care admission and monitoring. Dexmedetomidine is a known sedative that can help with patient symptoms and potentially reduce Clinical Institute Withdrawal Assessment of Alcohol Scale Revised (CIWA-Ar) scores. This study looks at two groups - the use of dexmedetomidine versus non-dexmedetomidine - in the management of AWS patients to assess the length of intensive care unit (ICU) stay, length of stay (LOS) in hospital, mortality and readmission rate. Methods: This retrospective cohort study was conducted at Geisinger Health System, a tertiary care, academic health care system, between January 2016 and December 2022. This study did not require ethics approval according to the Institutional Review Board (IRB). The patients assessed included those aged 18 years or older with a documented CIWA-Ar score requiring admission to the ICU or ICU step-down unit. Exclusion criteria included ICU admission for alternative indications, a prior history of seizure disorder, or patients who left against medical advice. Results: A total of 994 patients were identified to have met the inclusion criteria; 371 patients were in the non-dexmedetomidine group and 623 in the dexmedetomidine group. Primary outcomes assessed were hospital LOS (5.0 (3.0 - 9.0) vs. 10 (6.0 - 16.0), P < 0.0001) and days in the ICU (1.9 (1.0 - 3.4) vs. 4.9 (2.9 - 9.0), P < 0.0001), respectively. Secondary outcomes assessed were lorazepam equivalent usage, and readmission within 30, 60 and 90 days, which were not statistically significant when accounting for ICU LOS. Conclusions: Among patients treated for AWS, dexmedetomidine use appeared to be associated with longer hospital and ICU stays, while benzodiazepine requirements remained unchanged. These associations may reflect differences in illness severity, emphasizing the need for future prospective evaluation.
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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 | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".