Management of Uncomplicated Acute Alcohol Intoxication at a Mass Gathering Event: Stop the Intravenous Fluids
Bibliographic record
Abstract
Introduction: Uncomplicated acute alcohol intoxication (UAAI) requiring medical management is common at some mass gathering events. Most of the mass gathering literature reporting on medical management involving UAAI are single case studies. The common clinical practice for UAAI at mass gatherings reported in the literature involves intravenous fluids and antiemetics. However, emergency department evidence suggests that administration of intravenous fluids does not enhance patient outcomes, and in some cases extends emergency department length of stay and costs. Method: Using a retrospective cohort design of routinely collected data over a nine-year period (2010-2013 and 2016-2020), this study was set at an annual end-of-year ‘schoolies’ youth mass gathering event. The primary study aim was to determine the intravenous fluid management practices of UAAI at this event. Secondary study outcomes included patient demographic, clinical characteristics, and patient outcomes. Data were analyzed using time series and descriptive statistics. Ethical approval was obtained. Results: In total, 378 patients were identified with UAAI at the event over the nine-year period. The median patient age was 17 years (IQR: 17-18), with 47.2% (n=179) being male. Overall, the median length of stay was 74 minutes (IQR: 40 – 144). Only 7.9% (n=30) patients received intravenous cannulation and 6.3% (n=24) patients received intravenous fluids. Proportionately, the use of intravenous fluids for the management of UAAI decreased over the study years [2010, 28.6%; 2011, 32.1%; 2012, 15.6%; 2013, 6.3%; 2016, 2.6%; 2017, 0%; 2018, 1.8%; 2019, 0%; 2020, 0%]. Conclusion: Some mass gathering events have a higher incidence of UAAI presentations. This is particularly true for those mass gathering events with young adults and at music festivals. Knowledge translation from the emergency department context regarding UAAI clinical management could be applied to the mass gathering event setting. This clinical management should include a conservative approach to the management of UAAI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".