Bibliometric analysis of research on manual therapy for low back pain from 2013 to 2023
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is one of the most common symptoms prompting patients to seek treatment. Manual therapy is widely used to treat LBP. Nevertheless, there is a scarcity of bibliometric analyses examining the worldwide utilization of manual therapy for the treatment of LBP. METHODS: This research used the Online Bibliometric overview Platform website (https://bibliometric.com), CiteSpace (6.2.R4), and VOSviewer (1.6.19) to provide a comprehensive analysis of the current status and prospective developments in the field. The Web of Science Core Collection (WOSCC) database was searched for publications from August 1, 2013, to August 1, 2023 on manual therapy of low back pain. RESULTS: Among the identified articles, 488 fit the criteria. The number of papers on manual therapy for LBP has progressively risen over in the past 10 years, whereas the average number of citations of these papers has decreased. The leading countries producing publications on this discipline were the USA, Canada, and China. There were 345 authors of the studies, with Christine M. Goertz having the most publications. The University of Southern Denmark was the institution that contributed the most to the field. The Journal of Manipulative and Physiological Therapeutics published many of the research papers in this field. Keyword analysis showed that the relationship between low back pain, spinal manipulation, and management has been present throughout the development of this research area. CONCLUSIONS: Spinal manipulation, management, randomized controlled trials, Physical therapy, care and disability are the current research hotspots in the treatment of LBP with manual therapy. In addition, research on complementary medicine and clinical practice guidelines may become an important trend in the future.
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
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 | 0.014 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.189 | 0.305 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".