Screening for harmful substance use in emergency departments: a systematic review
Bibliographic record
Abstract
BACKGROUND: Substance use-related emergency department (ED) visits have increased substantially in North America. Screening for substance use in EDs is recommended; best approaches are unclear. This systematic review synthesizes evidence on diagnostic accuracy of ED screening tools to detect harmful substance use. METHODS: We included derivation or validation studies, with or without comparator, that included adult (≥ 18 years) ED patients and evaluated screening tools to identify general or specific substance use disorders or harmful use. Our search strategy combined concepts Emergency Department AND Screening AND Substance Use. Trained reviewers assessed title/abstracts and full-text articles for inclusion, extracted data, and assessed risk of bias (QUADAS-2) independently and in duplicate. Reviewers resolved disagreements by discussion. Primary investigators adjudicated if necessary. Heterogeneity precluded meta-analysis. We descriptively summarized results. RESULTS: Our search strategy yielded 2696 studies; we included 33. Twenty-one (64%) evaluated a North American population. Fourteen (42%) applied screening among general ED patients. Screening tools were administered by research staff (n = 21), self-administered by patients (n = 10), or non-research healthcare providers (n = 1). Most studies evaluated alcohol use screens (n = 26), most commonly the Alcohol Use Disorders Identification Test (AUDIT; n = 14), Cut down/Annoyed/Guilty/Eye-opener (CAGE; n = 13), and Rapid Alcohol Problems Screen (RAPS/RAPS4/RAPS4-QF; n = 12). Four studies assessing six tools and screening thresholds for alcohol abuse/dependence in North American patients (AUDIT ≥ 8; CAGE ≥ 2; Diagnostic and Statistical Manual of Mental Disorders, 4th Edition [DSM-IV-2] ≥ 1; RAPS ≥ 1; National Institute on Alcohol Abuse and Alcoholism [NIAAA]; Tolerance/Worry/Eye-opener/Amnesia/K-Cut down [TWEAK] ≥ 3) reported both sensitivities and specificities ≥ 83%. Two studies evaluating a single alcohol screening question (SASQ) (When was the last time you had more than X drinks in 1 day?, X = 4 for women; X = 5 for men) reported sensitivities 82-85% and specificities 70-77%. Five evaluated screening tools for general substance abuse/dependence (Relax/Alone/Friends/Family/Trouble [RAFFT] ≥ 3, Drug Abuse Screening Test [DAST] ≥ 4, single drug screening question, Alcohol, Smoking and Substance Involvement Screening Test [ASSIST] ≥ 42/18), reporting sensitivities 64%-90% and specificities 61%-100%. Studies' risk of bias were mostly high or uncertain. CONCLUSIONS: Six screening tools demonstrated both sensitivities and specificities ≥ 83% for detecting alcohol abuse/dependence in EDs. Tools with the highest sensitivities (AUDIT ≥ 8; RAPS ≥ 1) and that prioritize simplicity and efficiency (SASQ) should be prioritized.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".