Instruments to Detect Alcohol and Other Drug Misuse in the Emergency Department: A Systematic Review
Bibliographic record
Abstract
CONTEXT: Alcohol and other drug (AOD) misuse by youth is a significant public health concern. Unanticipated treatment for AOD-related morbidities is often sought in hospital emergency departments (EDs). Screening instruments that rapidly identify patients who require further diagnostic evaluation and/or brief intervention are critically important. OBJECTIVE: To summarize evidence on screening instruments that can assist emergency care clinicians in identifying AOD misuse in pediatric patients. METHODS: Fourteen electronic databases (including Medline, Embase, and PsycINFO) and reference screening were used. Psychometric and prospective diagnostic studies were selected if the instrument focused on detecting AOD misuse in patients aged 21 years or younger in the ED. Two reviewers independently assessed quality and extracted data. Validity and reliability data were collected for psychometric studies. Instrument performance was assessed by using sensitivity, specificity, and positive (LR(+)) and negative (LR(-)) likelihood ratios. Meta-analysis was not possible because of clinical and measurement heterogeneity. RESULTS: Of the 1545 references initially identified, 6 studies met inclusion criteria; these studies evaluated 11 instruments for universal or targeted screening of alcohol misuse. Instruments based on diagnostic criteria for AOD disorders were effective in detecting alcohol abuse and dependence (sensitivity: 0.88; specificity: 0.90; LR(+): 8.80) and cannabis use disorder (sensitivity: 0.96; specificity: 0.86; LR(+): 6.83). CONCLUSIONS: On the basis of the current evidence, we recommend that emergency care clinicians use a 2-question instrument for detecting youth alcohol misuse and a 1-question instrument for detecting cannabis misuse. Additional research is required to definitively answer whether these tools should be used as targeted or universal screening approaches in the ED.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".