International Orthopaedics journal: A bibliometric analysis during 1977-2022
Bibliographic record
Abstract
Objective. We aimed to examine the current research trends published by the International Orthopaedics (INOR) journal using bibliometric analysis. Design/Methodology/Approach. Using the Scopus database, we have retrieved all articles published by the International Orthopaedics journal from 1977 to 2022. The key players, such as countries, institutes, and authors, were identified, and their collaborative linkages were analyzed using MS Excel and VOSviewer software. Results/Discussion. We identified 7645 publications from 107 countries, of which 40 were from Europe and 32 from Asia. The most contributing countries were China, Germany, and France. The Netherlands, Canada, and Switzerland were the most impactful countries regarding citations. Hospital Henri Mondor (France) and IRCCS Rizzoli Orthopaedic Institute (Italy) were the most productive organizations. The most cited organizations were Harvard Medical School (USA) and Klinikum der Universität München (Germany). The most productive authors were Hernigou P (n=91) and Scarlat MM (n=56), and the most cited ones were Mont MA and Rouard H. The most active research areas were “Fracture Fixation” (n=1189), “Hip Arthroplasty” (n=1129), and “Osteosynthesis” (n=754). Hip received the most attention (n=2008), followed by Knee (n=1548), Spine (n=775), and Shoulder (n=517). 128 (1.67%) papers received >100 citations (high-cited papers or HCP) with an average of 150.11 citations per paper (CPP). Giannoudis PV and Mont MA published the maximum number of HCP. Conclusion. INOR has become a popular destination for global Orthopaedic researchers and is publishing their research from all the continents. The total number of publications in it has been progressively increasing and is receiving a more significant number of citations, thus helping to improve the journal's ranking and reputation.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.140 | 0.207 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".