Variability in the unregulated opioid market in the context of extreme rates of overdose
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Drug checking uses analytical chemistry technologies to report on the composition of drugs from the unregulated market to reduce substance use-related risks, while additionally allowing for monitoring and reporting of the supply. In the context of an overdose crisis linked to fentanyl, we used drug checking data to examine variability within the illicit opioid supply. METHODS: In this time-series analysis, data was collected from a drug checking service in Victoria, Canada from November 2020 to July 2021. Drugs reported as opioids by participants of the service (N = 454) were analyzed to determine sample composition and paper spray mass spectroscopy was used to quantify low-concentration actives. Interquartile and statistical process control (SPC) analysis, namely standard deviation control charts, were used to examine the degree of variability among samples. RESULTS: Fentanyl was found in 96% of samples reported to be opioids, with a median concentration of 9%. Concentrations varied significantly, with a standard deviation of 7% for fentanyl and where nearly 20% of data points fell outside the control limits. Over half of the samples contained an additional and unexpected active, most commonly etizolam (43% of samples). Etizolam also showed a large level of variability, uncorrelated to that of fentanyl. CONCLUSIONS: Based on our chemical quantification and SPC analysis, a high degree of variability was found in opioid samples from the unregulated market in both the drugs detected and the concentrations of those drugs. This demonstrated the opioid crisis to be less attributable to a bad batch of drugs but rather the general variability found in the unregulated market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it