Mapping the Evolution of Fentanyl Research: A Scientometric Analysis of Clinical Applications and Abuse Trends (1964–2025)
Bibliographic record
Abstract
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.
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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 | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.112 | 0.166 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".