Global scientific production in the field of knee arthroplasty: A cross-sectional survey of research activities
Bibliographic record
Abstract
Objective To determine the quantity and quality of articles in the field of knee arthroplasty worldwide and elucidate the characteristics of global scientific production. Methods Web of Science was used to identify articles in the field of knee arthroplasty from 2011 to 2015. The total number of papers, number of papers per capita, total number of citations, and mean number of citations were collected. Results In total, 11,590 papers were identified. The number of publications significantly increased from 2011 to 2015. Most originated from North America, East Asia, and West Europe. Most (88.51%) were from high-income countries, 11.48% were from middle-income countries, and only 0.01% were from lower-income countries. The United States had the most articles and total citations. Sweden had the highest mean citations, followed by Denmark and Canada. However, when adjusted by population size, Denmark had the most articles per million population, followed by Switzerland and the Netherlands. Conclusions The number of knee arthroplasty publications has rapidly increased in recent years. The United States is the most prolific, but some European countries are more productive relative to their population.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.026 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".