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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Machine Learning Algorithms Predict Achievement of Clinically Significant Outcomes After Orthopaedic Surgery: A Systematic Review
Kyle N. Kunze, Laura M. Krivicich, Ian M. Clapp, et al.
Arthroscopy The Journal of Arthroscopic and Related Surgery (2021) Vol. 38, Iss. 6, pp. 2090-2105
Closed Access | Times Cited: 42

Showing 1-25 of 42 citing articles:

Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion
Laith Alzubaidi, Khamael Al-Dulaimi, Asma Salhi, et al.
Artificial Intelligence in Medicine (2024) Vol. 155, pp. 102935-102935
Open Access | Times Cited: 10
Meaningless Applications and Misguided Methodologies in Artificial Intelligence–Related Orthopaedic Research Propagates Hype Over Hope
Prem N. Ramkumar, Michael Pang, Teja S. Polisetty, et al.
Arthroscopy The Journal of Arthroscopic and Related Surgery (2022) Vol. 38, Iss. 9, pp. 2761-2766
Closed Access | Times Cited: 26
MCID and PASS in Knee Surgeries. Theoretical Aspects and Clinical Relevance References
Ahmed Mabrouk, Benedict U. Nwachukwu, Ayoosh Pareek, et al.
Knee Surgery Sports Traumatology Arthroscopy (2023) Vol. 31, Iss. 6, pp. 2060-2067
Closed Access | Times Cited: 15
Recommended Requirements and Essential Elements for Proper Reporting of the Use of Artificial Intelligence Machine Learning Tools in Biomedical Research and Scientific Publications
Mark P. Cote, James H. Lubowitz
Arthroscopy The Journal of Arthroscopic and Related Surgery (2024) Vol. 40, Iss. 4, pp. 1033-1038
Closed Access | Times Cited: 5
Machine learning in shoulder arthroplasty
Tim Schneller, Moritz Kraus, Jan Schätz, et al.
Bone & Joint Open (2025) Vol. 6, Iss. 2, pp. 126-134
Open Access
Enabling Personalized Medicine in Orthopaedic Surgery Through Artificial Intelligence
Nickelas Huffman, Ignacio Pasqualini, Shujaa T. Khan, et al.
JBJS Reviews (2024) Vol. 12, Iss. 3
Closed Access | Times Cited: 4
Artificial Intelligence for Clinically Meaningful Outcome Prediction in Orthopedic Research: Current Applications and Limitations
Seong J. Jang, Jake Rosenstadt, Eugenia Lee, et al.
Current Reviews in Musculoskeletal Medicine (2024) Vol. 17, Iss. 6, pp. 185-206
Open Access | Times Cited: 4
Orthopaedic surgeons display a positive outlook towards artificial intelligence: A survey among members of the AGA Society for Arthroscopy and Joint Surgery
Marco‐Christopher Rupp, Lukas B. Moser, Silvan Hess, et al.
Journal of Experimental Orthopaedics (2024) Vol. 11, Iss. 3
Open Access | Times Cited: 4
Machine learning models predicting risk of revision or secondary knee injury after anterior cruciate ligament reconstruction demonstrate variable discriminatory and accuracy performance: a systematic review
Benjamin Blackman, Prushoth Vivekanantha, Romana Mughal, et al.
BMC Musculoskeletal Disorders (2025) Vol. 26, Iss. 1
Open Access
Ceiling Effect of the Combined Norwegian and Danish Knee Ligament Registers Limits Anterior Cruciate Ligament Reconstruction Outcome Prediction
R. Kyle Martin, Solvejg Wastvedt, Ayoosh Pareek, et al.
The American Journal of Sports Medicine (2023) Vol. 51, Iss. 9, pp. 2324-2332
Open Access | Times Cited: 9
Predicting Outcomes in Hip Arthroscopy for Femoroacetabular Impingement Syndrome
Andrew D. Spencer, Mia S. Hagen
Current Reviews in Musculoskeletal Medicine (2024) Vol. 17, Iss. 3, pp. 59-67
Open Access | Times Cited: 3
Attitude of aspiring orthopaedic surgeons towards artificial intelligence: a multinational cross-sectional survey study
Johannes Pawelczyk, Moritz Kraus, Larissa Eckl, et al.
Archives of Orthopaedic and Trauma Surgery (2024) Vol. 144, Iss. 8, pp. 3541-3552
Open Access | Times Cited: 3
Predicting Corrosion Damage in the Human Body Using Artificial Intelligence
Michael A. Kurtz, Ruoyu Yang, Mohan S. R. Elapolu, et al.
Orthopedic Clinics of North America (2023) Vol. 54, Iss. 2, pp. 169-192
Closed Access | Times Cited: 8
Editorial Commentary: Machine Learning Is Just a Statistical Technique, Not a Mystical Methodology or Peer Review Panacea
Prem N. Ramkumar, Riley J. Williams
Arthroscopy The Journal of Arthroscopic and Related Surgery (2023) Vol. 39, Iss. 3, pp. 787-789
Open Access | Times Cited: 7
Development of Machine-Learning Algorithms to Predict Attainment of Minimal Clinically Important Difference After Hip Arthroscopy for Femoroacetabular Impingement Yield Fair Performance and Limited Clinical Utility
Matthew Pettit, Sebastian Hickman, Ajay Malviya, et al.
Arthroscopy The Journal of Arthroscopic and Related Surgery (2023) Vol. 40, Iss. 4, pp. 1153-1163.e2
Open Access | Times Cited: 7
A Guide for the Application of Statistics in Biomedical Studies Concerning Machine Learning and Artificial Intelligence
Evan M. Polce, Kyle N. Kunze
Arthroscopy The Journal of Arthroscopic and Related Surgery (2022) Vol. 39, Iss. 2, pp. 151-158
Closed Access | Times Cited: 12
Using Machine Learning to Predict Nonachievement of Clinically Significant Outcomes After Rotator Cuff Repair
Rafael Krasic Alaiti, Caio Sain Vallio, Jorge Henrique Assunção, et al.
Orthopaedic Journal of Sports Medicine (2023) Vol. 11, Iss. 10
Open Access | Times Cited: 5
Editorial Commentary: Machine Learning in Medicine Requires Clinician Input, Faces Barriers, and High-Quality Evidence Is Required to Demonstrate Improved Patient Outcomes
Ayoosh Pareek, R. Kyle Martin
Arthroscopy The Journal of Arthroscopic and Related Surgery (2022) Vol. 38, Iss. 6, pp. 2106-2108
Open Access | Times Cited: 8
The Relationship Between Design-Based Lateralization, Humeral bearing design, Polyethylene Angle, and Patient-Related Factors on Surgical Complications after Reverse Shoulder Arthroplasty: A Machine Learning Analysis
Erick M. Marigi, Jacob F. Oeding, Micah J. Nieboer, et al.
Journal of Shoulder and Elbow Surgery (2024)
Closed Access | Times Cited: 1
Editorial Commentary: Knowledge is Power: A Primer for Machine Learning
Ian J. Wellington, James C. Messina, Mark P. Cote
Arthroscopy The Journal of Arthroscopic and Related Surgery (2023) Vol. 39, Iss. 2, pp. 159-160
Closed Access | Times Cited: 4
Deep Learning and Multimodal Artificial Intelligence in Orthopaedic Surgery
Anthony Bozzo, James M. G. Tsui, Sahir Bhatnagar, et al.
Journal of the American Academy of Orthopaedic Surgeons (2024) Vol. 32, Iss. 11, pp. e523-e532
Open Access | Times Cited: 1
The Definition of Failure in Hip Arthroscopy May Include Factors Outside of Reoperation: A Systematic Review
Christopher D. Bernard, Eva Bowles, Marcus Trotter, et al.
Arthroscopy Sports Medicine and Rehabilitation (2024) Vol. 6, Iss. 5, pp. 100962-100962
Open Access | Times Cited: 1
Enhancing Thoracic Surgery with AI: A Review of Current Practices and Emerging Trends
Mohamed Umair Aleem, Jibran Ahmad Khan, Asser Younes, et al.
Current Oncology (2024) Vol. 31, Iss. 10, pp. 6232-6244
Open Access | Times Cited: 1
Editorial Commentary: Machine Learning and Artificial Intelligence Are Tools Requiring Physician and Patient Input When Screening Patients at Risk for Extended, Postoperative Opioid Use
Yining Lu, Vikranth Mirle, Brian Forsythe
Arthroscopy The Journal of Arthroscopic and Related Surgery (2023) Vol. 39, Iss. 6, pp. 1512-1514
Closed Access | Times Cited: 3
Challenges and Opportunities for the Use of Patient-Reported Outcome Measures in Orthopaedic Pediatric and Sports Medicine Surgery
Kyle N. Kunze, Sophia Madjarova, Prakash Jaykumar, et al.
Journal of the American Academy of Orthopaedic Surgeons (2023)
Closed Access | Times Cited: 3
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