Uncomplicated Acute Alcohol Intoxication in the Emergency Department: A 12‐Year Retrospective Study to Understand Practice Change in Intravenous Fluid Usage and Patient Outcomes
Bibliographic record
Abstract
OBJECTIVE: This study aimed to identify uncomplicated acute alcohol intoxication (UAAI) practice changes in two Emergency Departments (EDs) and assess if these changes correlate with the conduct of a randomized controlled trial recommending practice change. METHODS: This dual-site retrospective observational cohort study used ED data over a 12-year period (2010 - 2021). The sample included patients with UAAI who presented to one health service in Australia. Changes in patient and health service outcomes were explored for UAAI ED presentations over time. Data were analysed using descriptive statistics, inferential statistics, and a monthly time series analysis. RESULTS: There were 2344 UAAI-related presentations during the study period. The time series identified two practice change points leading to three periods of practice change. The proportion of patients who received intravenous fluids (IVFs) decreased from 45.9% to 21.7% (p < 0.001). Most patients who presented with UAAI were discharged from the ED (69.4%, n = 1627/2344). Discharged patients had a reduced median length of stay over the study periods (p < 0.001). CONCLUSIONS: Changing practice from an interventional approach for the administration of IVFs to an observation-based approach, for ED patients with UAAI is appropriate and can be sustained. This approach for the management of UAAI is supported by an established and growing evidence base. Factors regarding the reach, adoption and translation of evidence to clinical practice should be further explored.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".