Current trends in pain management: A bibliometric analysis for the 1980-to-2023 period
Bibliographic record
Abstract
There is currently no bibliometric analysis available regarding pain management (PM). The aim of this study was to monitor the advancement of the PM research field, demonstrate global productivity, identify the most highly cited studies, delineate collaborations between research areas and countries, and uncover new research topics and intriguing trends. A total of 16,216 articles on the subject of PM published between 1980 and 2023 were downloaded from the Web of Science database and analyzed using various bibliometric analysis methods. Trend keyword analysis, thematic evolution analysis, conceptual structure analysis, factor analysis, citation and co-citation analyses, and international collaboration analyses were conducted. The top 3 most active countries were the United States of America (n = 4021), the United Kingdom (n = 791), and Canada (n = 602). The most prolific author was Christine Miaskowski (n = 47). The most researched topics from the past to the present were chronic pain, opioids, analgesia/analgesics, cancer pain, postoperative pain, low back pain, opioid, cancer, acute pain, and self-management. Factor analysis identified key topics such as analgesia and various types of pain in the central factor, with additional subfactors including low back pain and physiotherapy, nursing, and postoperative anxiety and depression. In recent years, starting in 2020, trending research topics have shifted towards e-health, telemedicine, virtual reality, digital health, mental health, peripheral nerve blocks, erector spinae plane blocks, quadratus lumborum blocks, opioid use disorder, buprenorphine, musculoskeletal pain, COVID-19, cervicalgia, and interprofessional collaboration. In addition to Western countries with major economies in the field of PM (USA, Canada, Australia, and European countries), we identified China, India, and Turkey as research leaders. Our bibliometric analysis of 10,566 articles on PM reveals a significant growth in research, with recent trends focusing on e-health, telemedicine, virtual reality, and peripheral nerve blocks. These emerging technologies and personalized treatment approaches are shaping the future of PM.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.121 | 0.213 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".