Novel Psychoactive Substances: Systematic Review and Evidence-Based Analysis of Literature
Bibliographic record
Abstract
BACKGROUND: The research output within the discipline of novel psychoactive substances (NPS) has been evolving since the end of the last decade. The introduction of the concept of evidence-based Medicine led to a revolutionary growth of all fields of medical research. The enhancements of research quality were also paralleled by the development of tools for critical analysis of literature.MATERIALS & METHODS: The aim of the study is to assess the NPS research output, by means of evaluation of the level-of-evidence and the implemented statistical analyses. An extensive database of near 600 published manuscript was created; the papers were selected from the PubMed/Medline database by using pre-specified keywords. Each manuscript will be systematically scanned for; the first author, research institution, country, year of publication, type of study, statistical analysis, level-of-evidence, and journals of publication. Research efforts from the Middle East were also observed and quantified.RESULTS: Teams of NPS researchers included members in the range of one to twenty-nine, with and an average of 4.75 authors per publication. Research output was densely mapped in the developed countries including the UK (53%), US (19%), Italy (14%), Germany (14%), and Sweden (10%); the Middle East contribution was minimal (<1%). The top two research institutes were; King’s College London (UK) and Sapienza University of Rome (Italy). Studies included; Cross-sectional analyses (15%), Reviews (18%), and Analytic chemistry (36%). A considerable number of publications (34%) had no statistics at all, while only 14% had inferential statistics. Top journals of publication were; Journal of Psychopharmacology, Current Neuropharmacology, and Drug and Alcohol Dependence.CONCLUSION: Research output should always be assessed for quality control purposes. This study represents an innovative and systematic method of critical analysis of NPS literature. Future study efforts should be respondent to this study to achieve a better quality of research.
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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 | 0.061 | 0.195 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.040 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".