Background: Citation rate and journal impact factor have traditionally been used to assess research impact; however, these may fail to represent impact beyond the sphere of academics. Given that social media is now used to disseminate research, alternative web-based metrics (altmetrics) were recently developed to better understand research impact on social media. However, the relationship between altmetrics and traditional bibliometrics in orthopaedic literature is poorly understood. Purpose: To (1) assess the extent that altmetrics correlate with traditional bibliometrics and (2) identify publication characteristics that predict greater altmetrics scores. Study Design: Cross-sectional study. Methods: Articles published in The American Journal of Sports Medicine ( AJSM), The Journal of Bone and Joint Surgery, Clinical Orthopaedics and Related Research, Acta Orthopaedica, and Knee Surgery, Sports Traumatology, Arthroscopy between January 2016 and December 2016 were analyzed. Among the extracted publication characteristics were journal, number of authors, geographic region of origin, highest degree of first author, study subject and design, sample size, conflicts of interest, and level of evidence; number of references, institutions, citations, tweets, Facebook mentions, and news mentions; and Altmetric Attention Score (AAS). Multivariate regressions were used to determine (1) publication characteristics predictive of AAS and social media attention (mentions on Twitter, Facebook, and the news) and (2) the relationship between AAS and citation rate. Results: A total of 496 published articles were included, with a mean AAS of 8.6 (SD, 31.7; range, 0-501) and a mean citation rate of 15.0 (SD, 16.1; range, 0-178). Articles in AJSM (β = 19.9; P < .001), publications from North America (β = 8.5; P = .033), and studies concerning measure validation/reliability (β = 25.5; P = .004) were independently associated with higher AAS. Greater AAS score significantly predicted a greater citation rate (β = 0.16; P < .0001). The citation rate was an independent predictor of greater social media attention on Twitter, Facebook, and the news (odds ratio range, 1.02-1.03; P < .05 all). Conclusion: AAS had a significant positive association with citation rates of articles in 5 high-impact orthopaedic journals. Articles in AJSM, studies concerning measure validation and reliability, and publications from North America were positively associated with greater AAS. A greater number of citations was consistently associated with publication attention received on social media platforms.

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Background: Despite previous reports of improvements for athletes following hip arthroscopy for femoroacetabular impingement syndrome (FAIS), many do not achieve clinically relevant outcomes. The purpose of this study was to develop machine learning algorithms capable of providing patient-specific predictions of which athletes will derive clinically relevant improvement in sports-specific function after undergoing hip arthroscopy for FAIS.Methods: A registry was queried for patients who had participated in a formal sports program or athletic activities before undergoing primary hip arthroscopy between January 2012 and February 2018. The primary outcome was achieving the minimal clinically important difference (MCID) in the Hip Outcome Score-Sports Subscale (HOS-SS) at a minimum of 2 years postoperatively. Recursive feature selection was used to identify the combination of variables, from an initial pool of 26 features, that optimized model performance. Six machine learning algorithms (stochastic gradient boosting, random forest, adaptive gradient boosting, neural network, support vector machine, and elastic-net penalized logistic regression [ENPLR]) were trained using 10-fold cross-validation 3 times and applied to an independent testing set of patients. Models were evaluated using discrimination, decision-curve analysis, calibration, and the Brier score.Results: A total of 1,118 athletes were included, and 76.9% of them achieved the MCID for the HOS-SS. A combination of 6 variables optimized algorithm performance, and specific cutoffs were found to decrease the likelihood of achieving the MCID: preoperative HOS-SS score of ‡58.3, Tönnis grade of 1, alpha angle of ‡67.1°, body mass index (BMI) of >26.6 kg/m 2 , Tönnis angle of >9.7°, and age of >40 years. The ENPLR model demonstrated the best performance (c-statistic: 0.77, calibration intercept: 0.07, calibration slope: 1.22, and Brier score: 0.14). This model was transformed into an online application as an educational tool to demonstrate machine learning capabilities. Conclusions:The ENPLR machine learning algorithm demonstrated the best performance for predicting clinically relevant sports-specific improvement in athletes who underwent hip arthroscopy for FAIS. In our population, older athletes with more degenerative changes, high preoperative HOS-SS scores, abnormal acetabular inclination, and an alpha angle of ‡67.1°achieved the MCID less frequently. Following external validation, the online application of this model may allow enhanced shared decision-making.Disclosure: The authors indicated that no external funding was received for any aspect of this work. On the Disclosure of Potential Conflicts of Interest forms, which are provided with the online version of the article, one or more of the authors checked "yes" to indicate that the author had a relevant financial relationship in the biomedical arena outside the submitted work (http://links.lww.com/JBJS/G392).

Background: Failure to appropriately identify and repair medial meniscal ramp lesions at the time of anterior cruciate ligament (ACL) reconstruction (ACLR) may result in increased anterior tibial translation and internal rotation, increasing the risk for graft failure. Knowledge of the risk factors leading to the development of ramp lesions may enhance clinicians’ vigilance in specific ACL-deficient populations and subsequently repair of these lesions at the time of ACLR. Purpose: To perform a systematic review and meta-analysis of factors tested for associations with ramp lesions and to determine which were significantly associated with the presence of ramp lesions. Study Design: Systematic review and meta-analysis; Level of evidence, 4. Methods: PubMed, OVID/Medline, and Cochrane databases were queried in April 2020. Data pertaining to study characteristics and reported risk factors for ramp lesions were recorded. DerSimonian-Laird binary random-effects models were constructed to quantitatively evaluate the association between risk factors and ramp lesions by generating effect estimates in the form of odds ratios (ORs) with 95% CIs. Qualitative analysis was performed to describe risk factors that were variably reported. Results: The review included 12 studies with 8410 patients. The overall pooled prevalence of ramp lesions was 21.9% (range, 9.0%-41.7%). A total of 45 risk factors were identified, of which 8 were explored quantitatively. There was strong evidence to support that posteromedial tibial edema on magnetic resonance imaging (MRI) (OR, 2.12; 95% CI, 1.27-3.56; P = .004), age <30 years (OR, 2.02; 95% CI, 1.23-3.22; P = .002), and complete ACL tears (OR, 3.0; 95% CI, 1.41-6.20; P = .004) were risk factors for ramp lesions. There was moderate evidence to support that male sex (OR, 1.58; 95% CI, 1.36-1.83; P < .001) and concomitant lateral meniscal tears (OR, 1.54; 95% CI, 1.11-2.13; P = .009) were risk factors for ramp lesions. Chronic ACL injury (≥24 months) demonstrated minimal evidence as a risk factor (OR, 1.41; 95% CI, 1.14-1.74; P = .001). No significant associations were determined between contact injury or revision ACLR and the presence of ramp lesions. Conclusion: Significant associations between male sex, age <30 years, posteromedial tibial edema on MRI, concomitant lateral meniscal tears, complete ACL tears, injury chronicity, and the presence of ramp lesions were found. Contact injury and revision ACLR were not significantly associated with the presence of ramp lesions.