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Record W3108229145 · doi:10.1097/cxa.0000000000000095

Alcohol-Related Psychiatric Presentations at a University-Affiliated Emergency Department: A Descriptive Prevalence Study

2020· article· en· W3108229145 on OpenAlexaffvenueabout
Anees Bahji

Bibliographic record

VenueThe Canadian Journal of Addiction · 2020
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsQueen's UniversityUniversity of Calgary
Fundersnot available
KeywordsMedicineEmergency departmentTriageLogistic regressionOdds ratioPsychiatryAlcohol intoxicationPoison controlInjury preventionEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT Background: Several previous studies have explored patterns of emergency department (ED) utilization for alcohol-related physical conditions. However, the characteristics of those presenting with alcohol-induced psychiatric presentations are less clear. Aims: To describe the sociodemographic features of patients seeking ED care for alcohol-related psychiatric presentations, and to identify which factors are associated with psychiatric admission. Methods: The present paper describes a retrospective cohort study with multivariate logistic regression with adjusted odds ratios (AOR). Findings: Between 2015 and 2018, there were 4237 alcohol-related presentations (39% female, median age 35.0 years). The most common diagnoses were alcohol intoxication (57%) and alcohol withdrawal (20%). Time trend analysis showed a nonsignificant increase in the number of visits by fiscal year. About 3% of all visits required psychiatric admission. Predictors of psychiatric admission were arrival by ambulance (AOR = 1.77, 95% CI, 1.38–2.29); Canadian Triage Acuity Score ratings of 1 (AOR = 24.9, 95% CI, 8.21–85.8) or 2 (AOR = 3.67, 95% CI, 1.63–9.88); greater length of stay (AOR = 2.6; 95% CI, 2.0–3.3); and age (AOR = 1.62 per decade, 95% CI, 1.53–1.72). Conclusion: Several sociodemographic variables could support future decision-making tools to guide psychiatric management for persons presenting to the ED with alcohol-related problems. Contexte: Plusieurs études antérieures ont exploré les modèles d’utilisation des services d’urgence (SU) pour les conditions physiques liées à l’alcool. Cependant, les caractéristiques de ceux qui présentent des symptômes psychiatriques induites par l’alcool sont moins claires. Objectifs: Décrire les caractéristiques sociodémographiques des patients qui recherchent des soins à l’urgence pour des symptômes psychiatriques liées à l’alcool et identifier les facteurs associés à l’admission en psychiatrie. Méthodes: Le présent article décrit une étude de cohorte rétrospective avec régression logistique multivariée avec rapports de cote ajustés (RC). Résultats: Entre 2015 et 2018, il y a eu 4 237 présentations liées à l’alcool (39% de femmes, âge médian de 35.0 ans). Les diagnostics les plus courants étaient l’intoxication alcoolique (57%) et le sevrage d’alcool (20%). L’analyse des tendances temporelles a montré une augmentation non significative du nombre de visites par exercice. 3% de toutes les visites ont nécessité une admission psychiatrique. Les indicateurs de l’admission psychiatrique étaient l’arrivée par ambulance (RC= 1.77, IC à 95%: 1.38-2.29); Notes du score canadien d’acuité au triage (CTAS) de un (RC = 24.9, IC à 95%: 8.21-85.8) ou deux (RC = 3.67, IC à 95%: 1.63-9.88); durée de séjour plus longue (RC = 2.6; IC à 95% = 2.0-3.3); et l’âge (RC = 1.62 par décennie, IC à 95% = 1.53-1.72). Conclusion: Plusieurs variables sociodémographiques pourraient soutenir les futurs outils de prise de décision pour guider la prise en charge psychiatrique des personnes qui se présentent à l’urgence avec des problèmes liés à l’alcool.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.060
GPT teacher head0.342
Teacher spread0.282 · 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.

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

Citations1
Published2020
Admission routes3
Has abstractyes

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