Presentations of active substance use in the emergency department
Bibliographic record
Abstract
OBJECTIVES: To explore the most common clinical presentations of active substance users in our institution's Emergency Department (ED). METHODS: This was a retrospective chart review of all patients that were brought to the ED of King Saud University Medical City in Riyadh, Saudi Arabia thought to be actively using illicit substances, between January 2019 and December 2021. Those with incomplete data were excluded. RESULTS: A total of 582 patients were included in the study, 532 (91.4%) males, the majority were in the age group 21-30 years old (53.1%). Most patients were fully alert (n=405, 69.6%). Overall, cannabis was used by 349 (60%) of patients. Seventy-four patients presented to the ED because of motor vehicle collisions, the majority were males (98.6%), 35 (47.3%) were the driver of the vehicle and 40 (54.1%) were on cannabis. Males had 5.5 times more medical illness presentations and 10.8 times traumatic illness presentations when compared to females predominantly presenting with psychological illness presentations. CONCLUSION: Among Saudi users of illicit substances, the majority were young men with medical illness presentations. The rate of traumatic injuries / vehicular and road traffic accidents is at 15.3%, and cannabis and amphetamine were the most used substances. Screening for active substance use should be conducted using both patient histories and laboratory testing for all high-risk presentations and not solely based on clinical assessment.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".