“Pressed OXY M30 Pills, Great Press, Potent, Fast Shipping!!!”: Availability of Counterfeit and Pharmaceutical Oxycodone Pills on One Major Cryptomarket
Bibliographic record
Abstract
From 2018 to 2021, seizures of counterfeit oxycodone pills containing non-pharmaceutical fentanyl or other novel synthetic opioids increased significantly contributing to continuing increases in overdose mortality in Northern America. Evidence suggests that counterfeit pills are distributed through cryptomarkets. This article presents data regarding the availability and characteristics of oxycodone pills advertised on one major cryptomarket between January and March 2022. Collected data were processed using a dedicated Named Entity Recognition algorithm to identify oxycodone listings and categorized them as either counterfeit or pharmaceutical. Frequency of listings, average number of pills advertised, average prices per milligram, number of sales, and geographic indicators of shipment origin and destination were analyzed. In total, 2,665 listings were identified as oxycodone. 48.2% (1,285/2,665) of these listings were categorized as counterfeit oxycodone, advertising a total of 652,699 pills (93,242.7 pills per datapoint) offered at a lower price than pharmaceutical pills. Our data indicate the presence of a large volume of counterfeit oxycodone pills both in retail- and wholesale-level amounts mostly targeting US and Canadian customers. These exploratory findings call for more research to develop epidemiological surveillance systems to track counterfeit pill and other drug availability on the Dark web environment.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".