Effect of restricting the legal supply of prescription opioids on buying through online illicit marketplaces: interrupted time series analysis
Bibliographic record
Abstract
OBJECTIVE: To examine the effect on the trade in opioids through online illicit markets ("cryptomarkets") of the US Drug Enforcement Administration's ruling in 2014 to reschedule hydrocodone combination products. DESIGN: Interrupted time series analysis. SETTING: 31 of the world's largest cryptomarkets operating from October 2013 to July 2016. MAIN OUTCOME MEASURES: The proportion of total transactions, advertised and active listings for prescription opioids, prescription sedatives, prescription steroids, prescription stimulants, and illicit opioids, and the composition of the prescription opioid market between the US and elsewhere. RESULTS: The sale of prescription opioids through US cryptomarkets increased after the schedule change, with no statistically significant changes in sales of prescription sedatives, prescription steroids, prescription stimulants, or illicit opioids. In July 2016 sales of opioids through US cryptomarkets represented 13.7% of all drug sales (95% confidence interval 11.5% to 16.0%) compared with a modelled estimate of 6.7% of all sales (3.7% to 9.6%) had the new schedule not been introduced. This corresponds to a 4 percentage point yearly increase in the amount of trade that prescription opioids represent in the US market, set against no corresponding changes for comparable products or for prescription opioids sold outside the US. This change was first observed for sales, and later observed for product availability. There was also a change in the composition of the prescription opioid market: fentanyl was the least purchased product during July to September 2014, then the second most frequently purchased by July 2016. CONCLUSIONS: The scheduling change in hydrocodone combination products coincided with a statistically significant, sustained increase in illicit trading of opioids through online US cryptomarkets. These changes were not observed for other drug groups or in other countries. A subsequent move was observed towards the purchase of more potent forms of prescription opioids, particularly oxycodone and fentanyl.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".