The top 100 most-cited total knee arthroplasty publications
Bibliographic record
Abstract
The object is to objectively identify the 100 most influential scientific publications in total knee arthroplasty (TKA) and provide an analysis of their main characteristics. The Clarivate Analytics Web of Knowledge database was used to obtain data and metrics of TKA research. The search list was sorted by the number of citations, and articles were included or excluded based on relevance to TKA. The information extracted for each article included author name, publication year, country of origin, journal name, article type, and the level of evidence. These 100 studies generated a total of 35,399 cita- tions, with an average of 355.9 citations per article. The most-cited article was cited 1273 times. The 100 studies included in this analysis were published between 2000 and 2017. 23 different journals published these 100 publications. Majority of the publications were from United States (n = 52), followed by UK (n = 10) and Canada (n = 8). The most prevalent study designs were case series (n = 32) and cohort studies (n = 30). The 100 most influential articles in TKA were cited a total of 35,399 times. The study designs most prevalent were case series and cohort studies. This article serves as a reference to direct orthopedic surgeons to the 100 most influential studies in total knee arthroplasty. More than half of the studies are from North America, and three journals hold two-thirds of the 100 most cited publications on the topic.
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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.079 | 0.090 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".