Fifty Most Cited Articles for Femoroacetabular Impingement and Hip Arthroscopy
Bibliographic record
Abstract
Growing awareness of femoroacetabular impingement (FAI) and recent innovations in management have resulted in hip arthroscopy becoming one of the fastest-growing orthopedic subspecialties. The purpose of this study was to identify the 50 most cited articles related to the topic of FAI and hip arthroscopy and to analyze their characteristics. The overall number of citations within these articles ranged from 99 to 820. Citation density ranged from 4.41 to 74.55. Seven countries produced these articles with the majority attributed to the United States (n = 26) and Switzerland (n = 18). Clinical studies made up more than half of the top articles (n = 27). The Journal of Bone and Joint Surgery level of evidence most commonly encountered was level IV (n = 24), while the remaining articles were level III (n = 3). No randomized controlled trials or non-randomized controlled trials were encountered in this search. The level of evidence was not significantly correlated with the overall number of citations, publication year, or citation density. The current top 50 list provides orthopedic surgeons interested in hip arthroscopy with an up-to-date core list of the most cited articles in the scientific literature and represents a foundation to use to develop their knowledge regarding hip arthroscopy and FAI.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.037 | 0.032 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".