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ACE Reports
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ACE Reports
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Exact search includes articles which contain all of the selected filters. Broad search includes articles that have at least one of the selected filters for each category.
Exact search includes articles which contain all of the selected filters. Broad search includes articles that have at least one of the selected filters for each category.
General Orthopaedics
14-Year Follow-Up of Patients Who Underwent Either Anatomic Double-Bundle or Anatomic Single-Bundle Anterior Cruciate Ligament Reconstruction
14-Year Follow-Up of Patients Who Underwent Either Anatomic Double-Bundle or Anatomic Single-Bundle Anterior Cruciate Ligament Reconstruction
Sports Medicine
Comparison of Anatomic Double- and Single-Bundle Techniques for Anterior Cruciate Ligament Reconstruction Using Hamstring Tendon Autografts: A Prospective Randomized Study With 5-Year Clinical and Radiographic Follow-up
Am J Sports Med. 2016 May;44(5):1225-36
This study has been identified as potentially high impact.
OE's AI-driven High Impact metric estimates the influence a paper is likely to have by integrating signals from both the journal in which it is published and the scientific content of the article itself.
Developed using state-of-the-art natural language processing, the OE High Impact model more accurately predicts a study's future citation performance than journal impact factor alone.
This enables earlier recognition of clinically meaningful research and helps readers focus on articles most likely to shape future practice.
Comparison of Anatomic Double- and Single-Bundle Techniques for Anterior Cruciate Ligament Reconstruction Using Hamstring Tendon Autografts: A Prospective Randomized Study With 5-Year Clinical and Radiographic Follow-up
This report has been verified by one or more authors of the original publication.
Am J Sports Med. 2016 May;44(5):1225-36
Sports Medicine
A Five-Year Prospective Randomized Study of Anatomic Single vs. Double Bundle ACL Reconstruction
This study has been identified as potentially high impact.
OE's AI-driven High Impact metric estimates the influence a paper is likely to have by integrating signals from both the journal in which it is published and the scientific content of the article itself.
Developed using state-of-the-art natural language processing, the OE High Impact model more accurately predicts a study's future citation performance than journal impact factor alone.
This enables earlier recognition of clinically meaningful research and helps readers focus on articles most likely to shape future practice.
A Five-Year Prospective Randomized Study of Anatomic Single vs. Double Bundle ACL Reconstruction