Bibliometric and visualized analysis of scientific publications on rehabilitation of rotator cuff injury based on web of science
Bibliographic record
Abstract
Background Since the discovery of rehabilitation as an intervention for rotator cuff injury, its impact on the recovery of rotator cuff injury has attracted crucial attention, and the number of related studies is increasing worldwide. There were no bibliometric and visualized analysis studies in this field. This study aimed to investigate the research hotpots and trends in the rehabilitation of rotator cuff injury via bibliometric and visualized analysis and to identify the future development of clinical practice. Method The publications regarding rehabilitation of rotator cuff injury from inception to December 2021 were obtained from the Web of Science Core Collection database. The trends of publications, co-authorship and co-occurrence analysis and visualized analysis were carried out using Citespace, VOSviewer, Scimago Graphica software, and R Project. Results A total of 795 publications were included in this study. The number of publications significantly increased yearly. The United States published the highest number of related papers and the papers published by the United States had the highest citations. The University of Laval, the University of Montreal and Keele University were the top 3 most contributive institutions. Additionally, the Journal of Shoulder and Elbow Surgery was the journal with the highest number of publications. The most common keywords were “rotator cuff”, “rehabilitation”, “physical therapy”, “management”, and “telerehabilitation”. Conclusion The total number of publications has shown a steady upward trend. The cooperation between countries globally was still relatively lacking, and therefore it is necessary to strengthen cooperation between different countries and regions to provide conditions for multi-center, large sample, and high-quality research. In addition to the relatively mature rehabilitation of rotator cuff injury such as passive motion or exercise therapy, telerehabilitation has also attracted much attention with the progress of science.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.174 | 0.181 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".