Fentanyl and other New Psychoactive Synthetic Opioids. Challenges to Prevention and Treatment
Bibliographic record
Abstract
Synthetic opioids have played a significant role in the current opioid crisis in the United States (U.S.) and Canada and are a matter of concern worldwide. New psychoactive opioids (NPOs) are classified in the internationally recognized new psychoactive substances (NPSs) category. This group comprises compounds that may have been synthesized decades ago but appeared only recently in the illicit drug market. Such is the case of fentanyl, fentanyl analogs, and non-fentanyl opioids. Most NPOs have effects similar to morphine, including euphoria and analgesia, and can produce fatal respiratory depression. Here, we present an overview of the systemic and molecular effects of main NPOs, their classification, and their pharmacological properties. We first review the fentanyl group of NPOs, including the four compounds of clinical use (fentanyl, alfentanil, sufentanil, and remifentanil) and the veterinary drug carfentanil. We also provide essential information on non-medical fentanyl analogs and other synthetic opioids such as brorphine, etonitazene, and MT-45, used as adulterants in commonly misused drugs. This paper also summarizes the scarce literature on the use of NPOs in Mexico. It concludes with a brief review of the challenges to prevention and treatment posed by NPOs and some recommendations to face them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".