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Record W7117866334 · doi:10.17605/osf.io/v7a6w

Mapping the Evolution of Fentanyl Research: A Scientometric Analysis of Clinical Applications and Abuse Trends (1964–2025)

2025· other· W7117866334 on OpenAlexaboutno aff
Zengwei Kou

Bibliographic record

VenueOpen Science Framework · 2025
Typeother
Language
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAnesthesiologyPublic healthMultidisciplinary approachPain medicineSAFERHarmHarm reductionFentanyl

Abstract

fetched live from OpenAlex

Background: Fentanyl, a potent synthetic μ-opioid agonist, has been widely used in anesthesia and analgesia since the 1960s; however, its misuse has escalated into a global public health crisis. Purpose: To map the evolving landscape, identify key contributors and research hotspots, and highlight emerging trends in fentanyl-related research. Methods: We collected fentanyl-related publications from three major medical databases covering the period from the 1960s to June 2025. Tools such as CiteSpace, VOSviewer, and Bibliometrix were used to analyze publication trends and research focus. Results: The analysis identified over 110,000 publications, with the U.S. (6,939), South Korea (3,629), and China (3,385) being the top contributors. The leading institutions included the University of Toronto and Harvard Medical School, while Anesthesia and Analgesia and Anesthesiology were the most influential journals. Research has evolved from clinical applications (propofol, postoperative pain) to addiction themes (opioid use disorder, xylazine), with abuse-related publications increasing by 23.1% annually post-2016. The keyword analysis revealed three clusters: anesthesia (general anesthesia), analgesia (morphine), and abuse (drug overdose). Recent studies have focused on the neurobiological mechanisms, structural modifications, and preventive strategies of fentanyl, emphasizing harm reduction (syringe services) and novel treatments (monoclonal antibodies). Conclusion: This study underscores the shift in fentanyl research from clinical efficacy to public health challenges, particularly addictions. Future research should prioritize: (1) developing safer opioid analogs with reduced abuse potential, (2) elucidating the molecular mechanisms of dependence, and (3) integrating multidisciplinary approaches such as immunotherapy and neuromodulation to address the opioid crisis. These findings provide a roadmap for policymakers and researchers to mitigate the societal effects of fentanyl.

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 imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.056
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.1120.166
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.276
GPT teacher head0.529
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
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

Citations0
Published2025
Admission routes1
Has abstractyes

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