Bibliometric analysis of multimodal analgesia research in the perioperative period: trends, contributions, and emerging areas (2013–2023)
Bibliographic record
Abstract
Objectives: Multimodal analgesia (MA) is a cornerstone in perioperative pain management, enhancing pain relief and minimizing opioid consumption by targeting various pain pathways. This study conducts a bibliometric analysis of MA research from 2013 to 2023 to understand its development and impact on perioperative care. Methods: A comprehensive literature search of the Web of Science Core Collection (WOSCC) was conducted, covering publications from January 2013 to December 2023. Data were analyzed using VOSviewer and other bibliometric tools to identify publication trends, key contributors, and emerging research themes. Results: The analysis identified 1,939 studies on MA, with a notable increase in annual publications since 2020. The USA, China, and Canada were the leading contributors. Key terms like Non-Steroidal Anti-Inflammatory Drugs (NSAIDs), Enhanced Recovery After Surgery (ERAS), and Patient-Controlled Analgesia (PCA) were frequently associated with MA. Significant journals included the Cureus Journal of Medical Science and Anesthesia and Analgesia. Influential authors such as Richard D. Urman and Henrik Kehlet were highlighted for their contributions. The research showed significant advancements and growing global interest in MA. Conclusion: The study underscores the growing importance of MA in perioperative pain management, with significant contributions from leading countries and researchers. Future research should focus on optimizing pain management protocols, enhancing patient recovery, and reducing opioid dependency through MA.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.179 | 0.238 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".