Reported recreational drug and new psychoactive substance use versus laboratory detection of substances by high-resolution mass spectrometry in patients presenting to an emergency department in London with acute drug toxicity
Bibliographic record
Abstract
INTRODUCTION: Clinicians managing patients with acute recreational drug or new psychoactive substance toxicity typically depend on self-reported drug(s) used. This study compares patient self-report (and/or from other sources) to the substance(s) that were subsequently identified in serum. METHODS: A prospective sample of 1,000 adults presenting to a tertiary care, urban emergency department in London, United Kingdom, with acute recreational drug/new psychoactive substance toxicity was collected from 1 February 2019 to 2 February 2020. A total of 939 appropriate samples underwent qualitative analysis by high-resolution mass spectrometry with comparison to a database of drugs/metabolites. Data on the stated drug(s) used were extracted from the routine medical chart/records; results were batched by drug class, when appropriate, and analysis was performed using R software. RESULTS: Seven hundred and ninety-nine (85.1%) patients were male with a median (IQR) age of 34 years (27 to 42 years). Six hundred and thirty-five (67.6%) patients reported using two or more drugs. The median (IQR) positive predictive value of a self-report substance having been taken was 0.68 (IQR: 0.44-0.86); conversely, the median negative predictive value of a substance having not been taken was 0.90 (IQR: 0.53-0.95). There was variability in the accuracy of reporting. For example, self-reported opioid use had a 90.5% likelihood that opioids were detected on analysis, whereas hallucinogens were only detected in 18.8% of samples when use was reported. Individuals were also mostly accurate in not underreporting substances. For example, those not explicitly reporting gamma-hydroxybutyrate use were 97.5% truly negative. DISCUSSION: Overall, most users were relatively accurate in their self-report of what class of drugs they had used, although there was variability in this accuracy. However, other drugs were present even when not reported, for example, opioids with disproportionate detection of prescription and over-the-counter (non-prescription) opioids that were unreported. CONCLUSIONS: Self-report (and/or collateral reports) had overall relatively high concordance with the likelihood that a substance was, or was not, recently used. Therefore, clinicians can make initial treatment decisions based on the self-reported drug(s) used in most cases.
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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 | 0.005 |
| 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 | 0.000 |
| 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".