Bibliometric Analysis of Research Trends on Manual Therapy for Low Back Pain Over Past 2 Decades
Bibliographic record
Abstract
Purpose: Low back pain (LBP) is a prevalent musculoskeletal disorder, and manual therapy (MT) is frequently employed as a non-pharmacological treatment for LBP. This study aims to explore the research hotspots and trends in MT for LBP. MT has gained widespread acceptance in clinical practice due to its proven safety and effectiveness. The study aims to analyze the developments in the field of MT for LBP over the past 23 years, including leading countries, institutions, authoritative authors, journals, keywords, and references. It endeavors to provide a comprehensive summary of the existing research foundation and to analyze the current cutting-edge research trends. Methods: Relevant articles between 2000 and 2023 were retrieved from the Web of Science Core Collection (WOSCC) database. We used the software VOSviewer and CiteSpace to perform the analysis and summarize current research hotspots and emerging trends. Results: Through screening, we included 1643 papers from 2000 to 2023. In general, the number of articles published each year showed an upward trend. The United States had the highest number of publications and citations. Canadian Memorial Chiropractic College was the most published research institution. The University of Pittsburgh in the United States had the most collaboration with other research institutions. Long, Cynthia R. was the active author. Journal of Manipulative and Physiological Therapeutics was the most prolific journal with 234 publications. Conclusion: This study provides an overview of the current status and trends of clinical studies on MT for LBP in the past 23 years using the visualization software, which may help researchers identify potential collaborators and collaborating institutions, hot topics, and new perspectives in research frontiers, while providing new clinical practice ideas for the treatment of LBP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.106 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.252 | 0.272 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".