Multiple Drug Ingestion by Ecstasy Abusers in the United States
Bibliographic record
Abstract
The abuse of ecstasy-type drugs such as 3,4-methylenedioxymethamphetamine (MDMA) and 3,4-methylenedioxyamphetamine (MDA) is generally associated with young adults attending "Rave" parties. Little toxicological information has been reported regarding ecstasy usage by individuals undergoing monitoring in other settings in the United States. The goal of this study was to determine the prevalence and patterns of licit and illicit drugs in urine specimens of ecstasy users. A survey of laboratory data over the years 2005-2007 revealed that 198 urine specimens were confirmed positive (cutoff concentration 100 ng/mL) for MDMA and/or MDA from the following types of donors ( positive specimens): Correctional (159); Sports (19); Workplace (9); Pain Patients (8); and Special Test Requests (3). Of these, 122 (61.6%) were positive for MDMA and MDA, 70 (35.4%) were positive for MDMA, and 6 (3.0%) were positive for MDA. A majority (84.3%) of the specimens contained multiple drugs and/or metabolites in addition to MDMA and MDA. The median number of drugs/metabolites reported for these ecstasy users was 5 (range, 1-9). In addition to MDMA/MDA, the most commonly identified drug groups (%) were cannabis (THCCOOH) (61.6%); amphetamine/ methamphetamine (38.4%); benzoylecgonine (30.8%); diazepam-related (9.6%); opiates (7.1%); alprazolam (5.6%); and others (5.6%). Although multidrug ingestion appears to be common amongst ecstasy users, caution is recommended in interpretation. Illicit ecstasy in the United States and Canada frequently contains methamphetamine and other active substances, and multidrug use may not have been intentional.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".