Fifty top-cited classic papers in orthopedic elbow surgery: A bibliometric analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: The number of citations that a paper has received reflects the impact of the article within a particular medical area. Citation analysis concerning the most cited articles have been widely reported in orthopedic surgery and its subspecialties. However, which articles are cited most frequently in orthopedic elbow surgery is unknown. This study aimed to identify and analyze the characteristics of the 50 most cited articles in elbow surgery. METHODS: Science Citation Index Expanded was used to search for citations in 181 journals chosen according to the relevance for elbow publications. The 50 most cited articles in elbow surgery were identified. The title, authors, year of publications, article type, journal source, country, institution, number of citations, decade published, citation density and level of evidence were recorded and analyzed. RESULTS: The 50 most cited articles were published between 1950 and 2010. The 1980s was the most productive decade. The number of citations ranged from 388 to 124. All the articles were written in English and published in nine journals. The majority of articles originated from United States, followed by Canada and United Kingdom. Fracture was the most discussed topic. The majority of the top cited articles were clinical studies, with the remaining basic research. The most common level of evidence was level IV. CONCLUSIONS: Identification of the most cited papers in elbow surgery shows an insight into the historical development of elbow surgery and provides the foundation for further investigations.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.858 | 0.850 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it