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Record W4386954493 · doi:10.1016/j.etdah.2023.100055

Understanding the evolving nature of novel psychoactive substances: Mapping 10 years of research

2023· article· en· W4386954493 on OpenAlexaff
Alessandro Carollo, Ornella Corazza, Olivier Rabin, Aurora Coppola, Gianluca Esposito

Bibliographic record

VenueEmerging Trends in Drugs Addictions and Health · 2023
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicForensic Toxicology and Drug Analysis
Canadian institutionsWorld Anti-Doping Agency
Fundersnot available
KeywordsNoveltyData scienceField (mathematics)PhenomenonEvent (particle physics)Scientific literatureComputer sciencePsychologyEpistemologyBiology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.340
GPT teacher head0.526
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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