Bibliometric Analysis of Outpatient Hip and Knee Arthroplasty Research Evolution.
Bibliographic record
Abstract
Objectives: Total joint arthroplasty is an effective treatment for end stage osteoarthritis. As perioperative protocols are developed, outpatient arthroplasty has been gaining traction to facilitate earlier recovery and same day discharge. The aim of this manuscript is to analyze the trends in outpatient arthroplasty over a 17-year duration. This analysis seeks to predict emerging themes in the literature on patient optimization and outcomes in outpatient arthroplasty. Methods: This study conducted a literature review on outpatient arthroplasty with the Web of Science Core Collection over a 17-year period between 2005 and 2022. Bibliometric data was imported and analyzed with Bibliometrix and VOSviewer. Results: 198 articles were identified demonstrating an annual growth of 19.61% with notable bursts in 2017 and 2021. United States was the top global contributor followed by Canada and European nations. There were significant contributions across 219 institutions and 758 authors, with the Journal of Arthroplasty being the most productive and influential journals. Key themes identified include the feasibility of outpatient surgery, pain management, and perioperative complications and costs. Conclusion: This bibliometric analysis highlights the ongoing growth and development within outpatient arthroplasty since 2005. The United States remain the global leader within outpatient related arthroplasty research. Previous, current, and ongoing trends are highlighted within this field for further development as hotspots.
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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 | 0.011 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.158 | 0.196 |
| 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) | 0.006 | 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".