Comparison of Laboratory Confirmed Drugs in Acute Recreational Drug Toxicity Presentations to an Urban Hospital in London, UK, 2016/17 versus 2019/20
Bibliographic record
Abstract
INTRODUCTION: Novel Psychoactive Substance (NPS) use is increasingly prevalent and is often associated with severe acute recreational drug toxicity (ARDT). 258 UK deaths were attributed to NPS use in 2021. Confirmatory testing which identifies NPS is limited by expense and timeliness. We aimed to identify NPS and other recreational drugs in a sample of 1000 ARDT presentations to a central London hospital in 2019/20 and to compare these drugs to those identified from a previous cohort in 2016/2017. METHODS: We prospectively enrolled 1000 serum samples from ARDT presentations to St Thomas' Hospital between February 2019 and February 2020. Serum samples were deidentified and underwent qualitative analysis via mass spectrometry. Results were returned at the conclusion of testing and statistical analysis performed using 'R' (R Foundation for Statistical Computing). RESULTS: Twenty-eight unique NPS were detected in 2019/20, compared to 31 in 2016/17. Eight new NPS were detected in 2019/20: four benzodiazepines, two synthetic cannabinoid receptor agonists, one cathinone and one ketamine-analogue. No NPS opioids were detected in either cohort. Cannabis (16%,11% p = 0.02), ketamine (12%,7% p < 0.01) and opioids (57%,24% p < 0.01) were detected significantly more frequently in 2019/20 than in 2016/17, while alcohol (22%,49% p < 0.01), cathinones (1%,15% p < 0.01), GHB (14%,20% p < 0.01) and MDMA (9%,18% p < 0.01) were detected less frequently. CONCLUSIONS: Studies that utilise confirmatory testing to detect NPS in presentations of ARDT provide important information for public health interventions. More NPS benzodiazepines and fewer NPS cathinones were detected in 2019/20, following temporal trends of forensic detection throughout Europe and reinforcing the importance of identifying emerging drugs.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".