A bibliometric study on trends in chiropractic research from 1920 to 2023
Bibliographic record
Abstract
OBJECTIVE: An increasing body of evidence suggests a positive role of chiropractic in the treatment of neuro-musculoskeletal disorders. This study aims to explore current research hotspots and trends, providing insights into the broad prospects of this field. METHODS: A bibliometric review was conducted on all chiropractic articles included in the Web of Science Core Collection before December 31, 2023. RESULTS: Over the past century, the volume of research in the field of chiropractic has been fluctuating annually, with four peaks observed in total. The United States, Canada, Australia, and the United Kingdom are leading countries. Chu, Eric Chun-Pu is the author with the most publications, while Bronfort, Gert has the highest total citation count. The University of Southern Denmark has produced the most publications, while Queens University - Canada is the most central institution. The Journal of Manipulative and Physiological Therapeutics is the journal with the most publications and citations, while the Journal of the American Medical Association is the most central journal. The two most-cited articles were both authored by Eisenberg DM. Emerging keywords include "chronic pain" and "skills". The theoretical mechanisms and scientific basis of chiropractic, its clinical practice and safety, education and training, integration with other disciplines, and patient experiences and satisfaction are the frontiers and hotspots of research. CONCLUSION: This study integrates bibliometric analysis to summarize the current state of research and global network centers in the field of chiropractic, further highlighting the hotspots and trends in this field. However, Individual and national rankings should be interpreted with caution due to our focus on Web of Science rather than PubMed.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.107 | 0.209 |
| 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".