The Top 100 Most Cited Articles on Anterior Cruciate Ligament Reconstruction: A Bibliometric Analysis
Bibliographic record
Abstract
Background: The concept of anterior cruciate ligament (ACL) reconstruction (ACLR) has become widely accepted, gaining increased attention in recent years and resulting in many research achievements in this field. Purpose: The aim of this study was to determine which original articles on ACLR have been most influential in this field by identifying and analyzing the characteristics of the 100 most cited articles. Study Design: Cross-sectional study. Methods: Articles on ACLR were identified via the Thomson ISI Web of Science database on November 30, 2019. The 100 most cited articles were identified based on inclusion and exclusion criteria. The data extracted from each article for the subsequent analysis included title, date of publication, total citations, average citations per year (ACY), journal name, first author, institutions, themes, level of evidence, and keywords. Results: The total number of citations was 29,629. The date of publication ranged from 1975 to 2015. A majority of the articles originated from the United States (58%) and were published in the 1990s (32%) and 2000s (48%). The mean ACY was 18.43 ± 9.51. Of the selected articles, nearly one-half were published in the American Journal of Sports Medicine (42%). The most prolific co-author and first author were Freddie H. Fu (n = 13) and K. Donald Shelbourne (n = 5), respectively. The most productive institution was the University of Pittsburgh (14%). Material comparison (19%) and technique comparison (16%) were the 2 most popular themes. More than one-quarter of articles were level 4 evidence (37%). Moreover, the keywords ACL, ACL reconstruction, ACL rupture, knee joint, knee injuries, and human showed the highest degree of centrality. Conclusion: By analyzing the characteristics of articles, this study demonstrated that ACLR is a growing and popular area of research, with the focus of research varying through timeline trends. Studies on anatomic reconstruction and biomechanics might be areas of future trends.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.130 | 0.125 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".