Global trends and hotspots of randomized controlled trials in chronic pain therapy (1989–2024): A 35-year bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: Chronic pain persists as a significant global health challenge, underscoring the necessity for effective, evidence-based treatment strategies. This study aims to assess publication trends and identify research hotspots within randomized controlled trials (RCTs) pertaining to chronic pain therapy. METHODS: We conducted a bibliometric analysis utilizing data from Web of Science Core Collection, covering the period from January 1989 to September 2024. Eligible studies were extracted from, and analysis was performed by VOSviewer, CiteSpace, and R 4.3.3. RESULTS: Our analysis encompassed a total of 4206 publications sourced from 939 journals, authored by 20,068 individuals across 86 countries. The most cited document was "Clinical importance of changes in chronic pain intensity measured on an 11-point numerical pain rating scale." The USA led in both publication volume and citation frequency, followed by the UK and Canada. Harvard University emerged as the most prolific institution, with significant contributions from journals such as Pain and Pain Medicine. Notable authors included Moore R. Andrew and Manchikanti Laxmaiah. Keyword analysis revealed research hotspots in "low back pain," "management," and "double blind," while emerging research frontiers included "guidelines" and "hip," indicating areas for future inquiry. CONCLUSION: This bibliometric analysis of RCTs pertaining to chronic pain therapy elucidated trends and emergent themes within the field. The findings yield significant insights for clinical applications and serve as a reference for prospective research directions in chronic pain management. This analysis reveals a paradigm shift from pharmacological trials to multimodal interventions incorporating psychological outcomes.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.036 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.180 | 0.266 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".