Bibliometric Analysis of Foreign Orthopaedics Research in the Indian Journal of Orthopaedics (2007–2024): Trends, Impact and International Collaboration
Bibliographic record
Abstract
Abstract This bibliometric study comprehensively analyzed 1,281 foreign publications of the Indian Journal of Orthopaedics (IJO) from 2007 to 2024. The findings reveal a significant surge in international engagement, with a 105.0% absolute growth rate in cumulative foreign publications between the 2007–2015 and 2016–2024 periods. Foreign contributions exhibited a higher average citation rate (8.75 citations per paper) than the overall journal average of 8.39, indicating substantial impact. Geographically, Asian countries, specifically China, Turkey, and South Korea, were the leading contributors by volume. However, North American nations (USA and Canada) demonstrated superior citation impact, with Canada achieving the highest rate at 15.52 citations per paper. Thematic analysis identified ‘Trauma, Fracture and Dislocation’ and ‘Arthroplasty and Joint Replacement’ as the predominant research areas. A notable surge in COVID-19-related publications was observed between 2019 and 2024. Key influential entities include McMaster University and author M. Bhandari. Only 6.79% of foreign documents received external funding, suggesting a need for increased support for international research. Overall, the IJO has cemented its status as a vital global platform for impactful orthopaedic research.
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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 | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.112 | 0.195 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".