Sports Medicine Fellowship Training Significantly Increases Sports Case Volume: An Analysis of ACGME Case Log Data from 2006 to 2019

Ibitayo, Ayooluwa T.; Kale, Nisha N.; Miskimin, Cadence; Mulcahey, Mary K.

doi:10.1016/j.asmr.2021.07.028

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…19,28,48 Fellowship training has been shown to dramatically increase case volume in sports injuries. 20 However, MLKIs are an area many sports medicine fellowship-trained surgeons feel less confident in treating. 49 Ensuring MLKIs are treated by a surgeon trained in a fellowship program that provides adequate exposure to complex cases, including MLKIs, is important.…”

Section: Discussionmentioning

confidence: 99%

Long-term Outcomes of Multiligament Knee Injuries in American Football Players

Cain,

Mussell,

Crawford

et al. 2024

Am J Sports Med

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Background: Long-term outcomes for isolated anterior cruciate ligament (ACL) reconstructions in competitive American football athletes are well reported in the literature, but little data currently exist regarding multiligament knee injury (MLKI) reconstruction outcomes. Purpose: To examine patient-reported and return-to-sport outcomes of competitive American football athletes who underwent primary, single-staged, multiligament knee reconstruction. Study Design: Case series; Level of evidence, 4 Methods: We identified patients from our institution’s prospectively collected data repository between 2001 and 2020 who underwent single-staged surgical reconstruction of an MLKI sustained during competitive participation in American football. We assessed patient-reported outcomes at a minimum of 2 years after surgery using the International Knee Documentation Committee (IKDC) Subjective Knee Form and questions regarding surgical satisfaction and return to sport. Successful return to sport was defined as a return to preinjury level of competition. We summarized all outcome data and compared outcomes between 2-ligament and >2-ligament groups and between ACL-only MLKI injury and bicruciate MLKI injury groups using independent t test for IKDC scores and chi-square test for return to sport. Additionally, we evaluated predictors of postoperative IKDC scores using linear regression and predictors of return to sport using logistic regression. Results: Outcome data were successfully collected for 53 of 73 total eligible patients (73%; mean follow-up time, 7.7 ± 4.0 years; all male; mean age at surgery, 18.1 ± 2.7 years). The mean postoperative IKDC score was 84 ± 16. The most common level of preinjury competition was high school (n = 36; 68%), followed by college (n = 10; 19%). Seven patients did not return to sport competition at any level due to limitations from their knee surgery, and 82% of patients that attempted to return to preinjury level of sport were able to do so. A total of 50 patients (94%) were satisfied or very satisfied with their surgical outcome. The 2-ligament (n = 39) and >2-ligament (n = 14) groups did not significantly differ in IKDC scores ( P = .96) or proportions with successful return to sport ( P = .77). Similarly, the ACL-MLKI injury (n = 39) and bicruciate MLKI injury (n = 14) groups did not significantly differ in IKDC scores ( P = .89) or proportions with successful return to sport ( P = .77). Age and body mass index were not significantly associated with IKDC scores or successful return to sport at follow-up (all P > .05). Conclusion: This study may represent the largest cohort of competitive American football athletes evaluated for longitudinal outcomes after multiligament knee reconstruction. Despite the severity of these injuries, we found good knee-related function and that the large majority of athletes who attempted to return to sport were successful. The majority of athletes (94%) were satisfied with their operative treatment.

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Section: Discussionmentioning

confidence: 99%

Long-term Outcomes of Multiligament Knee Injuries in American Football Players

Cain,

Mussell,

Crawford

et al. 2024

Am J Sports Med

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“… 2 , 10 , 14 , 21 , 25 While the indication for these procedures is not provided by the ACGME data sets, this may be due to the shifting of the limited opportunities during the pandemic from residents to fellows, as they generally have less time allotted to develop mastery of a difficult visuospatial skill. 6 , 9 Prior to 2019-2020 academic year, orthopedic sports medicine fellows performed approximately 275% more shoulder ( P <.0001) surgeries than residents. 6 , 9 …”

Section: Discussionmentioning

confidence: 99%

“… 6 , 9 Prior to 2019-2020 academic year, orthopedic sports medicine fellows performed approximately 275% more shoulder ( P <.0001) surgeries than residents. 6 , 9 …”

Section: Discussionmentioning

confidence: 99%

Orthopedic surgery residents reported increased shoulder procedure volumes during the COVID-19 pandemic

Sherman,

Haddad,

Bridge

et al. 2023

JSES Reviews, Reports, and Techniques

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Discrete Dynamic Modeling Analysis of Data Mining Algorithm under the Background of Big Data in the Strategic Goal of Sustainable Development of College Physical Training

Li¹,

Liu²

2022

Wireless Communications and Mobile Computing

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With the popularization of intelligent devices and the rapid development of information technology, many valuable data become easier to obtain. After obtaining massive data, the problem of how to make more efficient and scientific use of these data is in front of us. Data mining is a deeper processing of data. It is aimed at mining the internal associations hidden in complex data and making the data give full play to its maximum value. At present, college physical training is also in urgent need of more scientific and modern upgrading and reform. Data mining is introduced into college physical training so that physical training data is no longer just a simple collection, query, and storage. This paper uses and introduces the basic content of Apriori algorithm. The actual college sports training data are collected for preprocessing, and the discrete dynamic model is established by using Apriori algorithm. Using this model, the actual data are deeply analyzed. Using the strong correlation subintegration results, the physical training of college students is optimized and reformed. The scores of college students’ physical quality are improved, and the goal of sustainable development of college physical training is achieved. Based on the data of 126 students in four classes of a major in 2020, this paper mines the frequent subsets with strong correlation in the data and finds out the relationship between the correlation and trust between subsets. In the 70-day experiment, the scores of college students’ comprehensive physical quality have been significantly improved, which proves that the discrete dynamic model established by Apriori algorithm data mining has a significant role in college physical training. The questionnaire survey results show that 83% of the college students are very satisfied with the discrete dynamic model-assisted physical training, and 16% of the college students are basically satisfied with the system. Apriori algorithm has significant advantages in dealing with big data. Single Apriori algorithm also has some defects. The efficiency of data mining, data security, and the accuracy of model analysis results need to be tested in practice.

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Sports Medicine Fellowship Training Significantly Increases Sports Case Volume: An Analysis of ACGME Case Log Data from 2006 to 2019

Cited by 3 publications

References 10 publications

Long-term Outcomes of Multiligament Knee Injuries in American Football Players

Long-term Outcomes of Multiligament Knee Injuries in American Football Players

Orthopedic surgery residents reported increased shoulder procedure volumes during the COVID-19 pandemic

Discrete Dynamic Modeling Analysis of Data Mining Algorithm under the Background of Big Data in the Strategic Goal of Sustainable Development of College Physical Training

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