100 Most Cited Articles in Orthopaedic Surgery
Bibliographic record
Abstract
BACKGROUND: Citation analysis reflects the recognition a work has received in the scientific community by its peers, and is a common method to determine 'classic' works in medical specialties. QUESTIONS/PURPOSES: We determined which published articles in orthopaedic journals have been most cited by other authors by ranking the 100 top-cited works. By analyzing characteristics of these articles, we intended to determine what qualities make an orthopaedic article important to the specialty. Finally, we determined if there was a change in level of evidence of studies on this list with time. METHODS: Science Citation Index Expanded was searched for citations to articles published in any of the 49 journals in the subject category "ORTHOPEDICS." Each of the 49 journals was searched separately using the "cited reference search" to determine the 100 most often cited articles. Each article was reviewed for basic information including year of publication, country of origin, source journal of the article, article type, and level of evidence. We categorized the journal article by field of research where possible. RESULTS: The number of citations ranged from 1748 to 353. The 100 most often cited articles in orthopaedic surgery were published in 11 of the 49 journals, spanning from general to more specific subspecialty journals. The majority of the papers (76) were clinical, with the remaining representing some type of basic science research. The most common level of evidence was IV (42 of the 76 studies). Of the 76 clinical articles, 27 introduced or tested classification systems or outcome measurement tools. CONCLUSIONS: Authors aiming to write a highly cited article in an orthopaedic surgery journal will be favored by language of publication, source journal, country of origin, and introduction of a classification scheme or outcome tool.
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 | 0.013 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.086 | 0.092 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".