Understanding the evolving nature of novel psychoactive substances: Mapping 10 years of research
Bibliographic record
Abstract
Novel psychoactive substances (NPS) is an umbrella term used to describe a heterogeneous group of compounds that mimic the effects of existing drugs and whose demand and use rapidly emerge, change, or even vanish in the drug market. The novelty of this global phenomenon and its dynamic nature represent major challenges for the scientific community that constantly requires timely evidence-based inputs. Our aim is to review the literature on NPS over the past decade and compare its temporal evolution according to the topics presented at the International Conference series on NPS which is the largest scientific event in the field (now at its 10th edition). Our analysis shows that although the themes being covered largely overlap with a previous scientometric review, some new clusters not yet consolidated in the scientific literature have recently emerged. We have also found the NPS Conference materials anticipate the scientific literature by approximately 2.5 years. Such findings provide new insights on the latest NPS trends while addressing existing knowledge gaps characterizing the rapidly evolving field of NPS and emphasizing the importance of timely information sharing at the global level.
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 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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.031 | 0.046 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".