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Record W4417017796 · doi:10.1111/1742-6723.70182

Uncomplicated Acute Alcohol Intoxication in the Emergency Department: A 12‐Year Retrospective Study to Understand Practice Change in Intravenous Fluid Usage and Patient Outcomes

2025· article· en· W4417017796 on OpenAlexaff
Jamie Ranse, Amy Sweeny, Gerben Keijzers, Stephanie Rae Hagan, Sharon Mickan, Matthew Brendan Munn, Alison Hutton, Michelle Buckland, Laura Hamill, Catherine Delany, Katie East, Julia Crilly

Bibliographic record

VenueEmergency Medicine Australasia · 2025
Typearticle
Languageen
FieldMedicine
TopicPoisoning and overdose treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRetrospective cohort studyClinical PracticeAlcohol intoxicationEmergency departmentMEDLINE

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.386
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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