Predictors of a Medical Condition Among Patients Presenting to the Emergency Department with Amphetamine-Type Stimulant Use
Bibliographic record
Abstract
Background: Patients presenting to the Emergency Department (ED) with amphetamine-type stimulant (ATS) use can exhibit a wide range of symptoms, ranging from mild agitation to life-threatening dysrhythmias. Early identification of patients at risk for more severe medical complications after ATS use is a key challenge in emergency care. Objective: To identify clinical and demographic predictors associated with a medical condition among patients presenting to the ED after ATS use. Methods: Retrospective cohort study of patients who presented to the ED with suspected ATS use at a large academic community hospital in Ontario from 1 September 2016 to 31 August 2017. Patients were screened using ICD-10 codes and included if they had a positive drug screen and clinical suspicion for ATS use. Our primary outcome was a composite of recognized complications of ATS toxicity. Predictor variables included age, sex, employment status, mental illness or substance use history, ED administration of benzodiazepines, antipsychotics, or physical restraints. Multivariable logistic regression was used to assess associations. Results: Of 1591 charts reviewed, 128 (8%) met the inclusion criteria. The median age was 29.5 years (interquartile range [IQR]: 23–36), and 50.8% were female. In adjusted analyses, benzodiazepine administration was significantly associated with a medical condition (Odds Ratio [OR] 3.33; 95% CI: 1.31–8.45; p = 0.011) as was employment status (OR 9.30; 95% CI: 1.00–86.03; p = 0.019). Conclusions: Benzodiazepine administration and unemployment were strong predictors of a medical condition among patients presenting to the ED after ATS use. These patients should undergo thorough physical examination and diagnostic testing to identify and manage potentially life-threatening conditions.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".